{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ea8f681a",
   "metadata": {},
   "source": [
    "## 1.导包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8fac7104",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import joblib\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import datetime\n",
    "import copy\n",
    "import sys\n",
    "# sys.path.append(r'C:\\Users\\ying.tu\\Desktop\\模型\\lightGBM_V5') #修改自己环境的包路径\n",
    "import perform as pf\n",
    "import lightgbm as lgb\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.feature_selection import RFE\n",
    "from hyperopt import fmin, tpe, hp, Trials\n",
    "from lightgbm.sklearn import LGBMClassifier\n",
    "from lightgbm_tool import *\n",
    "from lightgbm_tool import kfold_objective, tune_n_estimators_learning_rate, plot_n_estimators_learning_rate, tune_feature_num, LGBModelTuner\n",
    "from data_handle import train_test_split, del_nan, del_mode, slim, del_cat, get_dummied, get_hit_indices,read_from, get_flags, get_sorted_feature\n",
    "from data_handle import *\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.feature_selection import RFE\n",
    "from hyperopt import fmin, tpe, hp, Trials\n",
    "from lightgbm.sklearn import LGBMClassifier\n",
    "from lightgbm_tool import *\n",
    "from lightgbm_tool import kfold_objective, tune_n_estimators_learning_rate, plot_n_estimators_learning_rate, tune_feature_num, LGBModelTuner\n",
    "from data_handle import train_test_split, del_nan, del_mode, slim, del_cat, get_dummied, get_hit_indices,read_from, get_flags, get_sorted_feature\n",
    "from data_handle import *\n",
    "import toad"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b077374",
   "metadata": {},
   "source": [
    "## 2.导数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4b933d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\项目\\\\23.06.14_建模_北银济南美团渠道\\\\评分模型'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1f492e92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.ipynb_checkpoints',\n",
       " 'data_handle.py',\n",
       " 'lgb.ipynb',\n",
       " 'lgb_function.py',\n",
       " 'lightGBM.ipynb',\n",
       " 'lightgbm_tool.py',\n",
       " 'model_parser.py',\n",
       " 'perform.py',\n",
       " 'result',\n",
       " '__pycache__',\n",
       " '北银济南美团_建模文档_lgbm.xlsx']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3dbb70aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../华夏美团_1w_全量结果001.csv',index_col = 'cus_num',low_memory = False,encoding = 'utf-8',skiprows = [1])\n",
    "df.rename(columns = {'other_var1':\"if_pass\",\"other_var2\":\"flagy\"},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2081eb82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0612708018154313%\n"
     ]
    }
   ],
   "source": [
    "# 坏客率\n",
    "badper = sum(df['flagy'] == 1)/len(df['flagy']) * 100\n",
    "print(f'{badper}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7daf5d5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\n",
    "    'custApiCode', 'name', 'id', 'cell', 'swift_number', 'cus_username',\n",
    "    'code', 'sl_user_date', 'flag_score', 'flag_applyloanstr',\n",
    "    'flag_applyloannewsub', 'flag_applyapprovalevaluate',\n",
    "    'flag_interestprefer', 'flag_inforelationpro', 'flag_quantilelevel',\n",
    "    'flag_specialList_c', 'flag_fraudrelation_g', 'flag_score.1',\n",
    "    'flag_score.2', 'flag_score.3', 'flag_score.4','scoreafrevoloan', 'scorerevoloan','scoreconson', 'scorecashon'\n",
    "],\n",
    "        inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "25603f65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.020612708018154312"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_vars = ['if_pass', 'frg_group_num','user_date','flagy']\n",
    "X =  df.drop(columns=drop_vars)\n",
    "Y = df['flagy'].astype('int')\n",
    "Y.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aefcfb8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 判断样本bad_rate是否低于0.05，若低于则设置scale_pos_weight,用于处理不平衡样本,在lgbm训练中使用\n",
    "if Y.mean()<0.05:\n",
    "    scale_pos_weight = 0.05*len(Y)/len(np.where(Y==1)[0])\n",
    "else:\n",
    "    scale_pos_weight = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c41c5838",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 剔除缺失值与同值较高的、类别较多的、转换数据类型，节省内存\n",
    " \n",
    "X = del_nan(X, nan_ratio_threshold=0.90) \n",
    "X = del_mode(X, mode_ratio_threshold=0.95)\n",
    "X = slim(X)\n",
    "Y = slim(Y)        \n",
    "X = del_cat(X, cat_threshold=10)\n",
    "# X = get_dummied(X)\n",
    "\n",
    "hit_indices = range(0,1000000)\n",
    "# 数据划分\n",
    "X, X_test, Y, Y_test = train_test_split(X, Y, test_size=0.3, random_state=99)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec1ed5e",
   "metadata": {},
   "source": [
    "## 3.初步确定变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a93c33e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 初步确定chosen_feature，通过cv确定n_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f36c2ee8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 以flag不全为0的样本进行模型训练\n",
    "X = X.loc[X.index.isin(hit_indices)]\n",
    "Y = Y.loc[Y.index.isin(hit_indices)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "badfb4be",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_train = lgb.Dataset(X, Y, silent=True)\n",
    "fixed_params = {\n",
    "    'boosting_type': 'gbdt',\n",
    "    'class_weight': None,\n",
    "    'colsample_bytree': 0.8,\n",
    "    'importance_type': 'gain',\n",
    "    'learning_rate': 0.05,\n",
    "    'max_depth': 3,\n",
    "    'min_child_weight': 0.02,\n",
    "    'min_split_gain': 0,\n",
    "    #'n_estimators': 500,\n",
    "    'n_jobs': -1,\n",
    "    'num_leaves': 8, \n",
    "    'objective': 'binary',\n",
    "    'random_state': 7,\n",
    "    'reg_alpha': 0.0,\n",
    "    'reg_lambda': 0.0,\n",
    "    'subsample': 0.8,\n",
    "    'subsample_for_bin': 200000,\n",
    "    'subsample_freq': 0,\n",
    "    'scale_pos_weight':scale_pos_weight}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "adc1ef8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_result = lgb.cv(fixed_params, lgbm_train, num_boost_round=500, early_stopping_rounds=100, nfold=5, metrics='auc',seed=7)\n",
    "fixed_params['n_estimators'] = len(cv_result['auc-mean'])\n",
    "lgbm_model = LGBMClassifier(**fixed_params)\n",
    "lgbm_model.fit(X, Y, eval_metric='auc')\n",
    "origin_features = X.columns\n",
    "lgbm_importance = lgbm_model.feature_importances_\n",
    "\n",
    "# #保存特征重要性\n",
    "# chosen_feature_df = pd.DataFrame(origin_features)\n",
    "# chosen_feature_df['lgbm_importance'] = lgbm_importance\n",
    "# chosen_feature_df = chosen_feature_df.loc[chosen_feature_df.lgbm_importance>0]\n",
    "# chosen_feature_df.to_excel('chosen_feature_importance0606.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d36c7175",
   "metadata": {},
   "outputs": [],
   "source": [
    "#threshold 要自己写 一般控制数量在300-400 不然跑不动\n",
    "chosen_feature = get_sorted_feature(origin_features, lgbm_importance, threshold=0)\n",
    "\n",
    "X = X[chosen_feature]\n",
    "X_test = X_test[chosen_feature]\n",
    "\n",
    "#获取初步选出的特征的flag，获取当前特征对应flag不全为0的index，更新hit_indices\n",
    "#flags = get_flags(chosen_feature)\n",
    "hit_indices = range(0,1000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58cc79e4",
   "metadata": {},
   "source": [
    "## 4.使用rfe对变量重新排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "87eea82e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 129 features.\n",
      "Fitting estimator with 128 features.\n",
      "Fitting estimator with 127 features.\n",
      "Fitting estimator with 126 features.\n",
      "Fitting estimator with 125 features.\n",
      "Fitting estimator with 124 features.\n",
      "Fitting estimator with 123 features.\n",
      "Fitting estimator with 122 features.\n",
      "Fitting estimator with 121 features.\n",
      "Fitting estimator with 120 features.\n",
      "Fitting estimator with 119 features.\n",
      "Fitting estimator with 118 features.\n",
      "Fitting estimator with 117 features.\n",
      "Fitting estimator with 116 features.\n",
      "Fitting estimator with 115 features.\n",
      "Fitting estimator with 114 features.\n",
      "Fitting estimator with 113 features.\n",
      "Fitting estimator with 112 features.\n",
      "Fitting estimator with 111 features.\n",
      "Fitting estimator with 110 features.\n",
      "Fitting estimator with 109 features.\n",
      "Fitting estimator with 108 features.\n",
      "Fitting estimator with 107 features.\n",
      "Fitting estimator with 106 features.\n",
      "Fitting estimator with 105 features.\n",
      "Fitting estimator with 104 features.\n",
      "Fitting estimator with 103 features.\n",
      "Fitting estimator with 102 features.\n",
      "Fitting estimator with 101 features.\n",
      "Fitting estimator with 100 features.\n",
      "Fitting estimator with 99 features.\n",
      "Fitting estimator with 98 features.\n",
      "Fitting estimator with 97 features.\n",
      "Fitting estimator with 96 features.\n",
      "Fitting estimator with 95 features.\n",
      "Fitting estimator with 94 features.\n",
      "Fitting estimator with 93 features.\n",
      "Fitting estimator with 92 features.\n",
      "Fitting estimator with 91 features.\n",
      "Fitting estimator with 90 features.\n",
      "Fitting estimator with 89 features.\n",
      "Fitting estimator with 88 features.\n",
      "Fitting estimator with 87 features.\n",
      "Fitting estimator with 86 features.\n",
      "Fitting estimator with 85 features.\n",
      "Fitting estimator with 84 features.\n",
      "Fitting estimator with 83 features.\n",
      "Fitting estimator with 82 features.\n",
      "Fitting estimator with 81 features.\n",
      "Fitting estimator with 80 features.\n",
      "Fitting estimator with 79 features.\n",
      "Fitting estimator with 78 features.\n",
      "Fitting estimator with 77 features.\n",
      "Fitting estimator with 76 features.\n",
      "Fitting estimator with 75 features.\n",
      "Fitting estimator with 74 features.\n",
      "Fitting estimator with 73 features.\n",
      "Fitting estimator with 72 features.\n",
      "Fitting estimator with 71 features.\n",
      "Fitting estimator with 70 features.\n",
      "Fitting estimator with 69 features.\n",
      "Fitting estimator with 68 features.\n",
      "Fitting estimator with 67 features.\n",
      "Fitting estimator with 66 features.\n",
      "Fitting estimator with 65 features.\n",
      "Fitting estimator with 64 features.\n",
      "Fitting estimator with 63 features.\n",
      "Fitting estimator with 62 features.\n",
      "Fitting estimator with 61 features.\n",
      "Fitting estimator with 60 features.\n",
      "Fitting estimator with 59 features.\n",
      "Fitting estimator with 58 features.\n",
      "Fitting estimator with 57 features.\n",
      "Fitting estimator with 56 features.\n",
      "Fitting estimator with 55 features.\n",
      "Fitting estimator with 54 features.\n",
      "Fitting estimator with 53 features.\n",
      "Fitting estimator with 52 features.\n",
      "Fitting estimator with 51 features.\n"
     ]
    }
   ],
   "source": [
    "# 使用rfe对初筛的特征重新排序，选取排名前100/150（可修改）的特征\n",
    "\n",
    "# 以flag不全为0的样本进行模型训练\n",
    "X = X.loc[X.index.isin(hit_indices)]\n",
    "Y = Y.loc[Y.index.isin(hit_indices)]\n",
    "\n",
    "lgbm_model = LGBMClassifier(**fixed_params)\n",
    "rfe = RFE(lgbm_model, n_features_to_select=50, step=1, verbose=1) #终止时特征数50个，跑出的结果这50个都是1，因为跑到还有50个特征时终止了\n",
    "rfe.fit(X, Y)\n",
    "rfe_rank = rfe.ranking_\n",
    "rfe_lgb_df=pd.DataFrame(rfe_rank,index=chosen_feature)\n",
    "rfe_lgb_df = rfe_lgb_df.rename(columns={0:'rank_lgb_rfe'})\n",
    "lgb_rfe_features = rfe_lgb_df.loc[rfe_lgb_df.rank_lgb_rfe<=101].index.tolist() #这里取了前150个特征\n",
    "\n",
    "X = X[lgb_rfe_features]\n",
    "#获取rfe选出的特征的flag，获取当前特征对应flag不全为0的index，更新hit_indices\n",
    "#flags = get_flags(lgb_rfe_features)\n",
    "#hit_indices = get_hit_indices(Data, flags)\n",
    "#hit_indices = [i for i in range(len(Data))]\n",
    "hit_indices = range(0,1000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f1ad8ea",
   "metadata": {},
   "source": [
    "## 5.贝叶斯调参"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "384d9c62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████| 100/100 [03:43<00:00,  2.24s/trial, best loss: -0.21156709115053735]\n",
      "86\n"
     ]
    }
   ],
   "source": [
    "# 设置目标函数，设置使用的数据（训练集），参数为cv后的参数\n",
    "# 在贝叶斯调参过程中，样本量较大时可能会比较慢，这个时候可以考虑将n_estimators适当设置的小一些，比如设到50-100，learning_rate适当大些，\n",
    "# 后面手动再调节这个参数\n",
    "\n",
    "# 以flag不全为0的样本进行模型训练\n",
    "X = X.loc[X.index.isin(hit_indices)]\n",
    "Y = Y.loc[Y.index.isin(hit_indices)]\n",
    "\n",
    "fixed_params_bay = fixed_params.copy()\n",
    "ks_score = kfold_objective(X, Y, model='lgb', params=fixed_params_bay)\n",
    " \n",
    "# 定义参数搜索的域，值域应以具体样本适当调整，且值域的范围大些\n",
    "# 不同域的类型\n",
    "# choice：类别变量\n",
    "# quniform：离散均匀（整数间隔均匀）\n",
    "# uniform：连续均匀（间隔为一个浮点数）\n",
    "# loguniform：连续对数均匀（对数下均匀分布）\n",
    "para_space_mlp = {\n",
    "    'feature_num': hp.quniform('feature_num', 50, len(lgb_rfe_features), 1), #步长可修改,下限100需要调整，需要小于len(lgb_rfe_features)\n",
    "    'learning_rate':hp.loguniform('learning_rate',np.log(0.01),np.log(0.2)),\n",
    "    'max_depth': hp.quniform('max_depth', 3, 5, 1),\n",
    "    'num_leaves': hp.quniform('num_leaves', 2, 32, 1),\n",
    "    'min_child_weight':hp.quniform('min_child_weight',0,500,5), #该参数的值域适当大些\n",
    "    'min_child_samples': hp.quniform('min_child_samples', 1000, 3000, 10), #该参数与样本量相关 1000 -3000 也可以\n",
    "    'min_split_gain': hp.quniform('min_split_gain', 1, 10, 0.1),\n",
    "    # 'subsample':hp.quniform('subsample',0.6,1,0.1),\n",
    "    # 'colsample_bytree':hp.quniform('colsample_bytree',0.6,1,0.1),\n",
    "    'reg_alpha': hp.uniform('reg_alpha', 1, 10),\n",
    "    'reg_lambda': hp.uniform('reg_lambda', 1, 10)}\n",
    "\n",
    "# 进行贝叶斯调参\n",
    "trials = Trials()\n",
    "max_evals = 100 #max_evals迭代次数越大越慢，可设置合理的值\n",
    "best = fmin(ks_score, para_space_mlp, algo=tpe.suggest, max_evals=max_evals, \n",
    "           rstate=np.random.RandomState(7), trials=trials)\n",
    "#rstate=np.random.default_rng(seed)\n",
    "#rstate=np.random.RandomState(7)\n",
    "\n",
    "# 对贝叶斯调参后的所有参数，拟合计算训练测试ks、auc，寻找出效果最好且相差最小的那组参数\n",
    "trials_result = trials.trials\n",
    "\n",
    "# 获取best参数所对应的抵贷次数\n",
    "for i in range(0,max_evals):\n",
    "    param = trials_result[i]['misc']['vals']\n",
    "    param_1 = {k:v[0] for k,v in param.items()}\n",
    "    if param_1 == best:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e86b6cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存贝叶斯参数迭代过程\n",
    "fw = open(\"trials_result.txt\",'w+')\n",
    "fw.write(str(trials_result))\n",
    "fw.close()\n",
    "\n",
    "#读取贝叶斯参数迭代过程\n",
    "\n",
    "fr = open(\"trials_result.txt\",'r+')\n",
    "trials_result = eval(fr.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b610611",
   "metadata": {},
   "source": [
    "## 6.根据贝叶斯迭代过程作图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ba08964c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据贝叶斯每一次迭代的参数，对训练集、测试集的ks、auc作图\n",
    "ks_trains = []\n",
    "ks_tests = []\n",
    "auc_trains = []\n",
    "auc_tests = []\n",
    "space = []\n",
    "for i in range(0,max_evals):\n",
    "    param = trials_result[i]['misc']['vals']\n",
    "    param_1 = {k:v[0] for k,v in param.items()}\n",
    "    param_1['max_depth'] = int(param_1['max_depth'])\n",
    "    param_1['feature_num'] = int(param_1['feature_num'])\n",
    "    param_1['min_child_samples'] = int(param_1['min_child_samples'])\n",
    "    param_1['min_child_weight'] = int(param_1['min_child_weight'])\n",
    "    param_1['num_leaves'] = int(param_1['num_leaves'])\n",
    "    fixed_params.update(param_1)\n",
    "    feature_num = int(fixed_params.pop('feature_num'))\n",
    "    chosen_feature_1 = lgb_rfe_features[:feature_num]\n",
    "#    flags_1 = get_flags(chosen_feature_1)\n",
    "#    flags_1.remove('flag_graylist')\n",
    "    # hit_indices_1 = get_hit_indices(Data, flags)\n",
    "#    hit_indices_1 =[j for j in range(len(Data))]\n",
    "    hit_indices_1 = range(0,100000)\n",
    "\n",
    "    X_tr, X_te = X[chosen_feature_1], X_test[chosen_feature_1]\n",
    "    X_tr = X_tr.loc[X_tr.index.isin(hit_indices_1)]\n",
    "    Y_tr = Y.loc[Y.index.isin(hit_indices_1)]\n",
    "    X_te = X_te.loc[X_te.index.isin(hit_indices_1)]\n",
    "    Y_te = Y_test.loc[Y_test.index.isin(hit_indices_1)]\n",
    "    lgbm_tuner = LGBModelTuner(LGBMClassifier(**fixed_params), X_tr, Y_tr, X_te, Y_te, hit_indices_1)\n",
    "    result_ks = lgbm_tuner.get_model_result(fixed_params)\n",
    "    train_ks = result_ks['ks'][0]\n",
    "    test_ks = result_ks['ks'][1]\n",
    "    train_auc = result_ks['auc'][0]\n",
    "    test_auc = result_ks['auc'][1]\n",
    "    ks_trains.append(train_ks)\n",
    "    ks_tests.append(test_ks)\n",
    "    auc_trains.append(train_auc)\n",
    "    auc_tests.append(test_auc)\n",
    "    space.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0bfd044f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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I2o5+FUS5LEzylNwPGUEsNEjz9Y1vFO+//vX2JLZgiCnufmVIamZG7POlLwVe/nLzNuNAELbtVxBA+jKuDlfE8SBUgoiq8kweRLf2hO2rH4X57LNCvafI68oIYqFCXsjqczSCIaa4CEr2biGZpAliEOExVUHEDWFk6A9RFUQ+L37XQYSYoqwf9lm/IaYlSzKCyJACyAtQJQipIJYsAZYtM2eGmCDJQN6o3UzdpAmiX5NWPkVMJTxgfHyIYVRFTRJRPYhef5+0EcSzz3YniCFnpmUEsVChUxAHD4r6SatXi4vu6afj7VOnIEZJEGGIcmMFY9RxY9yjhgwnvva1Iq5tqomVVkQNMakDkm7r6/avehBh5DkMBdEtxDTkzLSMIBYqdAri0CHhP0S52HQ3UlBBjDrEFAVxirmNG0FI2Pb4qB4VUUNMvSoInQchSVX9e+CBaO3otk43RFEQQ0ZGEAsV8uZ45pn2soMHzaW6Ja680jwKDXaoow4x9QtdyAxIZ1vDYNuizeOWfRWXIHI58TdoD2KYE+UygsiQCphCTEuXhm+3a1f3faoKYpwJwqQgxm00Lr/HuGVfRfEg1BATEG+uyiAJYlClNrIspgypQFiIKQxhBJE2D6JfMzYYghjnEJP6f1wQV0EA8Wa7R50HMQwFMTcHNBptBcHcfqDWCJERxEKFSUF0I4gDB9pPvTLtMy0exN69wF139b59sAMZ1xDTIMIfo0AvBBFn9B11HsQwCEKW2ZAKwrSvIWemZQSxUCEvvoMH2zeKDDGZLjZZyXT3bv3nwRsuDR6E3O+yZfrPo2StjHuI6XBWEGkJMfVLEHIOklQQpn1JE/3YY4Fzzkk8My0jiIUK9Sbav1/I21pNKAhdJgcz8G//JtY3hZl0aaGjJgj5Pb/xDfEdAeCjH412Y+kUUZJtTQrjShBx50EAvYWYohJEmIczKAXRjSAkhpSZlhFErzgcSimXSuL1vn3tEUxYiGntWvHfRBBpCzGpbeqlFMPhkua60EJMcX/fNHgQUUNMEhlBpBzjXkrZtoFjjhGv9+1rz6IOy2I65hhR1mDnTv3naUxzVUlLPhK11zz5cfUgxlVB9BJiiqMgBulB9HuOo4aY1ONlBJEhMTgO8IIXiNfPPBNNQRQKwKpV8RTEqAlCZzT3muUyrh5EpiDC958GDyJuiMlxhvJ7ZgSxUGFSEN2ymNas6e5BpCXNFdBPdutVQYw6xNRrWLPX0e0ow6jM7Zh/WjwI037VtvZrUmchpgypgOO0b3TVg+g2US6MIOJ2xsP2IKK0SYVJQYyKIHoNa/ZKEKMMo0a9NtLgQajmdT8KggiYmopGSBlBZEgUtg1MTor0z7gKYs8efUaHTkGEPWBnGI8c7UdB6DwVdfm4YBxDTFEJYhgeRD4f3o5BDHRkmY1cLjohZQSRYox7KWU58lqxIrpJDQiCsG39KNKUFmpKDxyFB9HPRKpx9SCSMKmTDjf1oyAGHWKSyQ2m/Q5ioCOfBaG2pxshZR5EiiHnCrzkJcC6deNVSllKVMtqE8ShQ8DERDv11YQ1a8R/XZgp7sSytHsQaQsx9Qr5fWW7B+ktJBVuitrpDsOklusMQ0Go7elGSOOuIIjoMiL6KRE9SkQf0Hz+cSJ60P17hIgOKZ+9nYh+5v69Pcl29oVxLKUsa7wEFUS38BIQThBxq5+m3YNIm0ndK2R75fcZhxTtYYSYonoQwHAIQqr3hUAQRJQHcD2AywGsB3A1Ea1X12Hm9zLzWcx8FoBPArjZ3XYZgGsBnA/gPADXElGE3msEGEeCUDtylSC6hZeAaApCxmyjSuWwdfrFIBXEqD2IXsKa8ql4QPzrdJTh0mGY1LYtlFMuQjeYNEHECTGpijDhEu5JKojzADzKzNuZuQHgJgCvC1n/agBfcl9fCuAuZj7AzAcB3AXgsgTb2jvGkSBUab1ihSi1ceBANAWxZAmweLF+spxti30StfevHs/UjrB1+kWQtPoxqUftQezd2+7s3/zmaGHNfs7x3r3Al77UfT2JQYaueiWIuAoi6uNG06Qg1OUJl3BPkiBWAVCruu1xl3WAiNYCOA7AN+NsS0QbiOh+Irp/n1qVdJgYR4JQR9XLl4uLbMeOaAQBmFNddTerejzd+rrXg4SOtPo1qUcZYoobXujXQI2zzSBDV1Hb3W+xvlEThCTVHTuAG24Qry+9NHxfw8j+c5EkQZBmmUkPXQXgq8ws6TDStsy8mZnPYeZzVqxY0WMz+8Q4EkRQQQDiAo0SYgJETSaTBxG8WdXj6dohO+4kCSIYox7XEBPQLjgo/3eD+l17abfcfvly/ef9hKHCFMewFEQU/wEIJ4h+Ouwoj7wd5PFiIkmC2APgWOX9agBPGNa9Cu3wUtxtR4txJIigBwGIUEW/CkKO1iWiEITMmkojQRwOCkJtq9wmjpcht3n44XYo6/rrB5O1F6Y4hjVRbtQKIgyHOUHcB+AkIjqOiIoQJHBrcCUiOgXAEQD+W1l8J4BLiOgI15y+xF2WPti23wgcB+gUBBCNII4+GvjUp4RnoRv16RREWIipWBTbJ+lBDEpBjNqDUI/dT4hJpmi/+93i/fbt5s5eKhXLap+HqOpFRVw/otcsprghxHEkCB3pJ4TECIKZHQDvgujYtwH4MjP/mIiuI6IrlVWvBnATc9uOZ+YDAD4KQTL3AbjOXZY+DDHlbGDQKQggWogpbNSnG60D4Re6LMOdtAehtqlfk3pcFUSw3VH2pV4rxWK8Y+sQ1Y/odR7EuJrU3farot+wYQxEPDu9gZlvB3B7YNmHA+8/Ytj2cwA+l1jjBgX1JpuYGG1bokINncRVEGHQyX25PGz9pAminxGm3AZIhweh69SPPlrf8a5cCdx7b+e2YfsyHc+y2plgpvVXrhzcXIphhZgG4UFkIaYMRoyjglBDJxMTwKJF4n2/BGEyqcNCTMMmiMMxxNRN1UkEz3EUw1sliG5ZaXv3isoCr361eX9RMayJcoNQEPJ8FIuDnWuSEcSYQy1EN04EERwZSxURNYvJBFOa6ygVRD8eRPA8dSvaNgzEzWIKi1fHURCFgphQlsuZj23bwJYtwNlnR2tbmFk+rIlygwwxlUq9zTV58EHx+mtfE/3JI4/492s6HpARRKoxRCYfKIIjY0kQSSmIUYeY+vUg5HeSHWTaQkxR1tdtE5Ug5NP4gPBR8rZtgjyiEoQ0y1/2MqBS8WdGqcRkOl6rJdZPiwfRC0EAbcKVHk/UmdRh6wwIGUH0g3ElCNnWX/1VceN///vi/dlnd8806TbqS7sH0UutHrUUQ5ztk8AgTeqoIaaoCuyBB8T/s8+On0ob3GeUTjdI4MBoPYjJyf7mmkQtCpmFmMYE6o/TS+rfqCAvPFniO4gwo3HvXjGpDgD+4R86R31p9yB6CUGQMm8zzvZJYNgKotGIThA//CFQLgMnn9xWBy99KXDRReHzJhoN8afWFYpCEMEQoGzfqBRErwTRj4LICCLFGHcF0SvkhRwkxcPNg9CNMJNsaxToOvWw0XpYOCJOiEnCssyDoQceAM48s+3VdFs/2A7dk9nCOt1gGrJ87TjRitgN2oPIFEQGH9QLf5wIot8OzpTNcrh5ELoOJI0EIUfr69YBp53mH60PwqRWf1OTB9FqCbM16D9EyezRtaMfggCiFbGLoyCiPDCoVOqtH1CzoIB0FLl0kRFEP8gUhH+5yYMwHU92wEmGbfpNcw0qiLR4ELpRuS6WH3aNDtKD2L4deP75ToKIoiB07YjS6QZH3urrKL/RoD2Ifk3qTEEcZhhXgui3gzPNqNV1JmHHS/tEOd0Ic9QehOxMdG2QsXwVSYSYdOv/8Ifi/4tf7F/er4KIYlLHGZQEt89CTKFIdCb1YY9xJQjZ1iOPFM+CCKJbhU5TTZ40ZjH1Ow9iHEJMEkFDGRh8iCmoCIKzuF/yEvF/5UoR4upVQfQaYoqjINLiQWQm9WGKcSUIeeF997vteLX6161CZz4vUj8H4UHIGbrD9CCiFldMs0mt+w7dQky9KgjZcQGdiqDbMyCS9CB0IabDQUF0m5CZTZQbE4wrQehurLjQjQzjehCjmgcR1qbgtsEOJC0eRPA1oA8xhXUmg54HoUOSHkS/CiItBBFUEN0mZIaR/oCREUQ/GFeC0N1YcaEbGY6DBxGHIEwKYpS/temaYxbtjWNSR1EQunkQceb8pN2DGOQDg2Rb4z4n2qSEshDTmGNcCSIpBdFLiGnYHkSckt0mkzotCkI34jaZ1MVisia1CcViOKEw9+5B9JvFNGgPQj78Ku5zooNprvJ43bK3gq8TQEYQ/WBcCSIpBZHWEJMuBDGuISbT3Bu5PDiCVTuvQYSY4lYs7UYoamca14NIYxaT+j4qgmmuUY8HZASRaowrQQxLQRyuIaa0KIg4r3WhmkEoiG41l7opCPWzQXgQ/SpEE5IkiH5CTJkHkWKMK0EMSkGMQ5rroE3qtHoQpo42TEEMIs11714xSQ4AvvCFzky4bgrC9H2GEWIa9ES5YSmILMQ0JhhXghiUghhEmuso5kGEtSlsW2A8FIRuJF4u904Qamxc97sHs3BUFIsiHdcUlw8jiHw+PC253xDToDyIoIcQty+Q7QgWhQw7nqwwnBFEijGuBDEsBdHtEZWj8CD6jVGP2oOIQwrq62CISWY9BbfVHa+bBxFGEN0UW5jykdcGoCeYNKW5Fgrxjq2i0eg8d92OVy6L12khCCKqJNmQscS4EoRti9GKWnUzLqIoCKJoN1YWYoqOKGGlKCGmqNdulHLf3RREsE0qwtqkEoSujfK7pWGinJzwqbYrKoLXaLfj2Xbv4ayY6EoQRPRSItoKYJv7/kwi+rtEWzUuMN2saUccaW1CUEEwi1FenLTQpAlChiYWuklNJJ49bsp+6ceDAJJTEN06XbnPfhREHA9CfcRwcD8qmfVCEHEUhFw/n0+Fgvg4gEsB7AcAZn4IwMuTbNTYYFwVRJwbw4TgSFI3mgPCR9xJE4SuTYNQEGlOcw2+lr918HfohyCGoSCidLrD9iAAc6irH4LQ1dDqRhCSPFNAEGDm3YFFMWeCHKYYV4JIQkGYfI2wmH3SBJHECHMcPIggicj4eJT1dcdLgwcRRhC9JCFIdRmXIEzt6FdBxCEIVV0l3O9EOTu7ieilAJiIigDeAzfctOAxzg8MGrSCMGVGjTLEZMovVz8Lgy5GnSYPIuprXUHEJBTExETn9kl6ELoBQNTfVyqBQRBElLaGIa5JbfpNE0AUBfE7AH4XwCoAewCc5b7PIC8EovEiiGEqiFGGmHQEMe4hprgKQj3Hgwox9eJBmAiiHw+in4lyppCoCd0UxLBNat1vmgC69hLM/AyA6URbMa6QP44uxzzNGKaCMI1yWi1h+KXZgxhXkzqobHXhiDghpuA8CJmQILPgongQpvM9aA8iaietUx9hSDLE1I+CGDVBENHnAXRY98z8G4m0aJwgf5zJyfEiiGEriG4ZKEmNhMI8iF4VRJo8iG5lKgC/SR03xCQnuAU9CLlNFILopiD6IYh+Qohx5wKlyYNIE0EA+Dfl9SSANwB4IpnmjBmG+EMNFGnwINQbdBQeRK8TqUbtQTQaYkAyN9efSR2FILqF6GQufj8Kolu7o8yD6EVBpIkgTArCRKppMqmZ+WvqeyL6EoD/iLJzIroMwF8DyAP4e2b+mGadtwD4CIRKeYiZ3+YubwLY4q62i5mvjHLMoWJcCSIJBRFGEN1ubjXHXC030C/69SDSGmKqVDoJIgmTOuz86Tr2QSuIfjyIqApiUB5EEgqiVjOvn+TASm1GD9ucBGBNt5WIKA/gegAXQ5jb9xHRrcy8VVnnJAAfBHABMx8koqOUXdSZ+awe2jc8jCtBDEJBBNMd46a5qjeobItuol0/CBthjnOIqVwWzxLvx6SW6+Ry3TtvU4hJYn7e/5mKXhVEryGmUSiIfrOYbLv9LAn1eGEhpsnJdCgIInoeYnRP7v+9AN4fYd/nAXiUmbe7+7kJwOsAbFXW+W0A1zPzQQBg5qdjtX7UGFeCGISCCGaz9BtikssGSRCHq4LQ1eGJa1LL15VK7yGm4PGG7UH0oyAGbVL3k8WU4olyUUJMi3rc9yoA6gS7PYogQh4AACAASURBVADOD6xzMgAQ0XchwlAfYeavu59NEtH9ABwAH2PmW4IHIKINADYAwJo1XUXN4DGuBDFMBRE1xKQuGxSSTHMddDgsKkwEEVasL8ykDsvAGwRBDNuDiJvmmgYPIq5JnQYPgoheHLYhM/+wy751d08wG6oAEbJ6JYDVAL5DRKcz8yEAa5j5CSI6HsA3iWgLMz8WaMNmAJsB4Jxzzon5INgBYFwJYpgKoluIadgEEfVYppm28v2gw2FRoRJE1GJ9YSZ1FAURTHPVHc9U/LEfBTEx0T20k8u1S1+rxxsnD6KXNNcUeBB/GfIZA3hVl33vAXCs8n41OrOf9gD4HjPbAB4nop9CEMZ9zPwEADDzdiK6B8DZAB5DmjCuBDEoBSHTIPP5+GmuwyCIfjwIOdNWR3hy36MiiCOOaL9Wl0uYQkyS9FTfoVIBnn/efCyguwchOzidooqqIHRp01NT3U1qE4EPW0FMTi6sNFdmvrDPfd8H4CQiOg7AzwFcBeBtgXVuAXA1gC8Q0XKIkNN2IjoCQI2Z593lFwD4sz7bM3iMK0HITJh+oHa0alXJqNkYaQ8xmWLUSbU1KhqN9m8XJcSkzoOQ79XwYLkMHDhgPhYQLcSkCy+p63dTEEElE9WD6PX36dWD0BXrs21BZr2a1P1MlDNlOg0Ikc4OEZ0OYD3EPAgAADP/Y9g2zOwQ0bsA3AnhL3yOmX9MRNcBuJ+Zb3U/u8QtJ94E8IfMvN+t/fQZImpBlAP5mJr9lBqoP9Q4lfselIIA2hkVh5sHYQpB9NoJDAq2LUIvwfIuYbH8ctn/vYMEMQgPwkQQURVEkCCiehDB30eGuqL+vpkHEYooWUzXQngE6wHcDuByAP8FIJQgAICZb3e3UZd9WHnNAH7f/VPXuRfAGV1bP2qoBFGvj7o10TEoDwJo3+Dj4kFEzTRJq4IIy0rK5UQIyWRSA+12qx1znDRXkwfRjSCiKIhe0lx113GUVORBexD9ZDFJ0g4eLwUeRJRifW8C8GoAe5n51wGcCUBTtnEBYlxDTINUEPKmHhcPIpeLVlwxbF6H+vmwYbrmGg2RS09kNqnl9ur/uGmuYR6EDt0Um1wefOJd1IlyOoIwqdbgtnLdKEjapE6pBxGFIOrM3ALgENFiAE8DOD7RVo0LxpUgdJI2Lkwdju5CT1OISb4f5xCTLqwpR6G65bqOdtghprBqrpalN6mjeBC66ziKgujVg0hDiCkNJrWC+4loKYDPAvgBgFkA/5Noq8YF40oQg8jAiaog0hZiku/H1aQOUxDSWwgze00KQjevIyzNdZAKoljsnFcTxYMwhZjGUUHECTGlyYNg5ne6Lz9NRF8HsJiZH060VeMCmQ2ke8pWmjFsBTGOBNFNQYwyi0nXocqReDcFIbdRPQjT88TjeBC6hwUBwjAOhr2C30fX7l6zmIBosflBehBRyMwEWYMsroLQzW1JAF1DTET0r0T0NiKqMPOOjBwUqBf3OBHEMBVE2jwIoL8QRFo9CHUkriOI4DlWQ0zqexW6NNe4HgRR+P1hUhBRPYheQ0yDVhC9mtSmWeim+4bZn7qcApP6rwC8DMBWIvoKEb2JiCa7bbQgMK4hpoXuQfQTgkiTB6HrpHUdbZhJLYvEmUI4QH8eBNBJWir6URD9hJjS4kF0U94cKBChKp80KAhm/k83zHQ8RFmLt0AY1RnGmSCSUhBpT3OV78cxxKSGI3QEESXEpCqIfL79TIckCSLYpuAxTIooyjyIftNcB0kQsuRHLwShUxCASFsOHgtIjwcBAERUAnAFgLcCeDGALybZqLHBuBLEINJcTSPSNIWYDjeTWv0+piwmnUmthphUD0IlDl0HHseDWLLE3O4wj66b8unVg+h1AGBClHYQxQ/76MJ4weOpNa6C18CoCYKI/hmiCuvXIZ7vcI+b9pphXAkiCQWRxhBTmAfRawcySg8irHNQs5hMT5RT92Eyr03Hk4jrQcjtoyiIYIipW1zfFCodtoJQ76co5BTcFjArCMfxJwCoA5cheBBRzs7nAbyNmTVFSBY4xpUgklAQcQ3h4IWuLhsUwhREryb1KD0ItTPRpYXGDTH1QhC69efnu3sQcUxqacQmqSCS8CDkeoNWECqCg4QknsSoIIoH8fWMHAwIEkTQUEojTCmNcRFHQYw6xBQsQz0Ik3oUCkLtTKKY1LqOVg0xyfXVfavQjW4H7UHovBNZEK9QaP92afUg1KyiqMdWEbhvZrbMYN0n1iH33O9j3TXAzI9v0re7G7kPCFGymDKYEByF6So9pg1xY68mBEMNYR2qLDNtakdSYRt54wZHV4driMmy/BlDasnyXhREWDXXqLWYgPgKQr2WZPG9OCGmYU6Uk9d1vwqiWMTMlhlsuG0Ddj67Ewxg51Jgw7fei5ktM+31g9eAuiwBZATRD6LcZGlDXGltQrCjsO3Oh7eoxwneNMNSEKYR5uFgUgdDTMFSGyrJBRXEIENMg1YQQXIOy4Qb9US5INHEJQjlHG+8eyNqtr98d82pY+PdGzvXT/K+UWAkCCK6lIjepFk+TUQXJ9aiccI4EkSSCsLUGavHDbYjaYLo18Q0xYZH6UFEDTGpnYnOpFZDTFEJQmbqxCGIfhSE/B+HIPrxmExIiiAUBbHr2V3aVXzLU6Qg/gjAf2qW3w3gumSaM0ZQY49hqYJpQ5IKwiT3gXQRRD8mZpoUhMmM1vlCQaKOk+Ya9HCCxx6kgmDuPPem32uAISYv9v9HOaz7xDp/WEdti8k07jeLybKwZska7Sq+5SkiiDIz7wsuZOa9APp8HNlhgCGbRQPDsBWEacQ9rDRXk4LoNUadBg/CVIspbCTea4gpiofTr4II+njDUBDy83w+EPtn7Hx2JzbctsFPEpIkkwoxFYvY9OpNKOb857Gcn8SmV2/qPF4KCGKSiDrOPhFZAEqJtWhcMGQmHxiGrSBGHWLq1YPoFmJKWRbTzJpDWLf6q8it/wrWveZnonMLURAzS3Zi3cU/Qu5brxTZMk/e2Xk802+qdvitlthnPwoiGOoahAcR5fd1TXBt7N+u+WP/0l8zXce9ZjEpv+n0GdO44NgLvI/WHgI2v+QjmD5jur2+Lmw4Cg8CwM0APktEnlpwX3/a/WxhY1wJYlQKIk0hpkGY1CnyIGa2zGDDK57DzsIsmICdU44YAf/0K+31lXbPbJnBhhO2YmepIUbMS4EN2/+mM6wi/YEg1POnS4UNIo6CaDTiKYhePSZl8GCK/e98dqc/3KRrxwBNagCYtWcBAO844kLs+AQw/YLLzOsP4VoMI4j/A+ApADuJ6AdE9EMAOwDscz9b2BhXgghREF3jsCriehCjCDElaVKnwYNw32+8eyNqgWbW7Bo2/uDPxZuASb3x7o2o5f1pxzWe94+Y5fFM50/+7qZqpKb1dd8pqCDieBD9KgjAGPsH4A83JUEQyvlzWg62PL0FADAHx79/ibR4EMzsMPMHABwL4B0A3g5gDTN/gJnHoCdMGONKEIaOL1IcVkWw4+81iymfH74H0Y9JnQYPIkAQxuyX2pP+9QHAcaJlywD6R2HK/cm2RCGIbrWY+lEQ/aS5uttuevUm5ClvXNULNyWsIB7Z/wjmnDkAQJ2V+0p3vFETBBG9kYjeCOByACcBOBHAOUS0KLHWjBOChqG6LM0wdHyR4rAqZJ3/frKY5MNk0uxBpDnE5J57Y/ZL6WjxImBSR8qWkcfr5kEkoSDieBD9JCG4277t9LdhyeQSlApma3XXs7v0nX+/WUzK+Xtw74MAgKniVHcFoUtdTgBhIaYrAn9XAngfgIeJ6FWJtWhccJgpiMijShXBjJm4BKHeVLp1+kU/HsQYhZg2XfhRlAP9b9kqY9MLf9e/PgA4Dja9ehPKjj8zqUxFf7aMPF5UBWF6ohyQnIIICzHF8CAeO/gYDtQP4C8u+QusXbJWu/qaJWvCFUSvJrXymz7w5AMo5ot40coXoc6BSYOa9Uc6UY6Zf13z9zoArwTwp4m1aFwQzCgBxoMgDAoi8qhSRVBBxE1zTTNBdDOp05LmyozpU96MzbcBORDAbvbLFZsxvfIisX7A0Jw+Yxqbv78SU02x7AXPAZsXTfuzZeTxBuFBhD0wKKoHoTnfMyfPYd3U33d6ZlE9Jnf/d2+/GwBw0fEXCfK0yr5Vy1ZZkGeSIaZiEQ8+9SBOP+p0LJ5YjHorAkGMWEFowcw7AfSZAnMY4DBTEKE3RgCemf17h7BuyefbKZVx01zlTdXLg1aioJ8sF5OCGKUHoRuUNJtAo4HpLcAUJnA0TYnsl/VX+TuvwDU6/WgJv1IV5H/HDDBdOLvzeHEUhNvBaxMdTITMHF1BBLaf2TKDDZc3sTP3XKdnFtOkvvvxu7F68WqctOwkQZ5XbPaURJ7y+MxrPyPIM0GTmgsFPLj3QZy18iyUCiXMpd2DMIGITgEwn0BbxgvjShCGkfH0GdP49Gs+7b0nEK7/5es7RpU+M5uAnYVZcWMu3R0/zVV+1suDVqIgoGq8zmvJJ7DuN58Pz9KS5yk4izisumjSMF1zbidTh426jF03Gv71pd8jz7Fto54X1YfrBei/jynN1eBBGBMdSo/qFYSumGBED2Lj3R9CLdA0zzOLOgCwLLS4hW8+/k1cdPxFIHdC4PQZ09hxzQ780xv+CU1u4sRlJ4ptElQQT8w/g2dqz+DsY87GZGEy/QqCiG4jolsDf/8F4HYAf5BYi8YF40oQIfMgzl99PgDgTevfBAZj9eLVHesYzey1P8PM6oOdo8coISYgOYJwv2cHsS3h8Cwt1URXYZowNQyYrjnbRpMAG03UoXTcwd9aHVk3Gqi7qa51C+ZSGzEUhPHamPiOfv+q+lDLjkfodHc9u7tzf1DM5AgKYua4Waz+q9XYX9+P2356W8e18JqTXwMrZ+HmbTcb2zEok/rBg1sBAGcd7SqIbgSRZHq4gjAF8RcA/lL5+wsA/w+AU5n53sRaNC4YV4IImQexdZ+4SN95zjtRzBdx56Nidq0aNtj57E7tbndOzGPDqT/rHD3uv0esEKYgZHsSJIjYWVqB8JQvdPJ7LczwQ4NtaxTorrlGQ3T28i2aaBL0sXx1ZG3bqOdcgjApiLA0V9m5z7vBhLBic/ScUAvBku8hhDdzBrDu3jeL8/2L38fM8id8m67RDF4A1zOzLH2JeQUzle3YcO6TeHJWpALvr+/vGDAsnVyKVx33Kty87WYwc6IK4oF9Yv7Di1a+yFUQ8/79B9YfuYJg5v8M/H2bmX8M4Fwiuj6xFo0LxpggZs4A1n37DR3mniSIc15wDn5pzS/h6499vSNsYEKe0Tn5yq5h4+OfFW9GQRDKMWJnaSnhqY7QyRJgg/X18BBVEgjpUOvKqZwroDPEBPhHt7aNek6EeOoW9NduzDRXY6IDlvrbL6FTELaNmaf+AxuuAHbOPSXO9+QcNpz6mO98b3rph/WZW9JMBkKvp43HbEWt4L+edQOGVYtW4bGDjyF/XV6UMJl63L8jxxH30/feKu6nF96BmVUHjMftQKOBmTMJf/JfIu/nRZ96EXYc2oF600AQ8ng3novcLWeLMinPfCv68WIikgdBRGcR0Z8R0Q4AfwzgJxG3u4yIfkpEjxLRBwzrvIWIthLRj4noRmX524noZ+7f26Mcb6gYU4KY2fdNcfPV93aYe1v3bcWaJWuwaGIRLjvxMvzo6R/h/Xe9v2PkHUTZKotRqwY7554SF/Heu/wfBLOe4krzKFA6uFWLV2lXMWZpKQpCqz7IMauPpGCae6MoCEAJGQVHt2poyLZRcwmiNpmPRxCGENM1v3CNttk7cUhcAw8HCNWgiDb+/B87/YVCy3e+p096IzbfBiyjdmKF55lFIIhdxbp+uTJgmNkygy/96EsAIO6Vio0Nxz7oI6qZvXf5ycyqYcP5T0UePMzwQ9jwWkbdEe3Z+exO3LX9LtSadTEcC3yHmer3xPGe39Muk7J3c2KDlTAP4mQi+jARbQPwtwB2AyBmvpCZP9ltx0SUB3A9xES79QCuJqL1gXVOAvBBABcw82kArnGXLwNwLYDzAZwH4FoiOqKXL5gYxpQgNu6dMZp7W/dtxfoV4idqtkTn8fPnf27eGQNr5yZF1kew1oOCnUuBDTs+6b+IhxxiOm/VeR0flwslbZaWt22v6iMpmFKrGw1fqY2aVARSLX7jcjG6fcdBzFjbvH3VSZzv+mR+IB7E09WnAYhRdxA7lwIbvvEu/dPRAoS3y35G+/V959txML0F2Fi+1Ft0/BHHt9un7l+DNfOT+uXKgGHj3Ru9jluilm/6iGrjni9qyIwjDx42Wt/pKJNit0S77Tw6zfn52zuPx43EBithCuInAF4N4ApmfplLCnGeqXkegEeZeTszNwDcBOB1gXV+G8D1zHwQAJj5aXf5pQDuYuYD7md3AQhUrRoxTPHglGOXY775tj2zDeuXr8fMlhlc9+3uj/z485+tw47v/yKmz5jGpodXYLJlvpw6av0EU1DjTjCKAtvGzNLdWPPxNbh5282wqH28tYeAzRd+vDP3X21fl1o9oXNEkkDEEFPdDTHNHPhPMdqsPSlGm4ta2LDk25h56AaAGXVyQ0wTPSgIZR7EzBnA2m/8Mv70v/4UpUIJ//fi/6udcNbxdDQd4TUaWFM4Uvv1dc9FqFM7rPntnd8WLyIoiE2PrUO56b9eg2ndUQYGkcgsBLtyzxs/myug05zHob6OFxdhBPErAPYC+BYRfZaIXg3AEEjQYhWE6pDY4y5TcTKAk4nou0T0PSK6LMa2IKINRHQ/Ed2/b1/HoyuShRyd3fXLyH1urZDQz393uG3oAWvy+pvvmEXHYM6Zw2lHnaYNqaiQ8yVqRfJu8untFXxo/3rjNkDnCDBpBTGz9jlsWH4vdj8nLiXbzS2/yDpFzBU4PjheUaAQ2HvOf0/Hx+VW3qw+koJtt7OoAgqiI8Rk29i4/8udo81cExu/KTppmfFUL+Z69iBm9n8LG64AdtWF2Vt36p5fo4P26WgBBbHpiDd1+gsOaZ+LUCMHhVwBL1r5IvznTvf5ZhHmqkw/cST+9tFTvPdrl6wVkwuVAUOUgUEkMgvBmuaU8bO6hiDW8JK+jhcXYSb1vzDzWwG8EMA9AN4LYCURfYqILomwbx2ZBF3OAkSdp1cCuBrA3xPR0ojbgpk3M/M5zHzOihUrIjRpcJjZf49/dLYU2PD8jcM3LmNi06LXa829q067CgCwfsX60NGIvJGK+WI7lAEAjoOXzx8DADiqcpR2W99FPASC2Hjuc16cXcV/OzvabTDBtjFzYh3rPrEOf/ANkdVt5UTHs3Y2j837fsGsPpKC2mEHs5g0CmJXc792N7ue2yPWc+dM1Cdy5hCTMg/Cy+Ra/Q9Yd9VezGyZwcb9X9GGLE3F73zXgEFBTJfOw+bbgIpbG2mVU8Lme4/0n2+PIGyUCiW8fM3Lce/ue2E3FW8rLOTrOLjieXG9fvLyT2LHNTs6fk/t5NFmzkdUm5a9ufN+shF58LBp/1mYDFyGxbw4oXULHdfopuYrOo8HK7HBSleTmpmrzDzDzK8FsBrAgwC0hnMAeyAqwUqsBvCEZp1/ZWabmR8H8FMIwoiy7UihHZ3BHr5xGRPTxZdg823AZF7UzslRDp9+zae9Tv3U5acaRyNrl6z1bqSyVRYEoZTakLHUd57zzs4bK1jrx3Ewc6wyb+L1j2Om/NhAv+uuRfo0xyrPe202Yab8GDa89BnfSJjBKBVK2HHLcZg+NOTwEqAnCBli0pjUa3LLtLtZ43oEUkHUitQ1zbVjHsnilnhvIKEmNzuvgeDT0Uymu+svXLb6QgDAXU9dhumfBQrpKQqibJXx8rUvR82u4YdP/jDabHfHEd8b6GinhJxVvXhiMQDXb/vRcT4imS6fj823AVMFsY9jWhVs/sZE5MHD9KFj8QdbRZYXgbB2yVr89ot/G4A+xDQ9fwo231EAuWPotYeAzcU3JTZYiTWT2vUEPsPMUYr13QfgJCI6joiKAK4CcGtgnVsAXAgARLQcIuS0HcCdAC4hoiNcc/oSd1lqYBydDdu4jAv35jvnqLORoxxa3MLapWux9ZmtWLVoFZZMLolUdqNslUWaoKIgZNrgG099IzZfsRmFnBjJrT0EbF7yq76LeOaIPdhw+uPt1NGKgw2rfjBQBbbmOX1EtERK52rAxhUPdaRBOi1HmJZJ+CVRYNuYOa0lSPW+14qw5u7b9QrCtrFp8jWdo81mHpvO/SDsHODAnQdhIogI80jyhi5EKs3JgjCD1x4CNp99rb8jc/2LdQ//BnKfXS2+z3Pf9c5ttSkM4qrFxvkANdgeQQCuDxFFQSgDmoplfoLy9BnTeN8vvg8A8OhDr8D07kCujHs/vX6tMMtvmX8Dpn8Uo1ttNHDBIVEg+97fvBc7rtmBi44XNbR0ISbYNqYfLSGfy2PV1AtEqJReFP14MRG71EZUMLMD4F0QHfs2AF9m5h8T0XVEdKW72p0A9hPRVgDfAvCHzLyfmQ8A+CgEydwH4Dp3WWqwJqdPqhq6cRkX7k1TdWp4+dqXYyI/ga9t/Zovg0mtRyNHNcH4bNkqi3kPioKoWi3vs+kzpnHaitNw5dpLxUVcfImvGRvXPdY5byLXHKgC23RPDhPsD3XkKId1haN850KHXZY+DRIAHCs/EoKYyW/FhotqbVJdCmz4yZ9h5um7tQpiOncmNt8GTLhqcU3NwubHT8f0uisC65sJYqayHes+sc7oKTTRQimwqRxMTJ8xjctPvBynT64V18DRF/u/z547RJi28XT7+1S/hJnZ/wYA1Jri2Qi+gYiEVBAsCGLl1EocXTka195zLXKP/Zogm8duMZ9Mx0HVarc3DD7PzTBRrubOW6jlW7EnylUnRDcsiUqWHdcpCNg2GhMFOC0HVafmLUsKiREEADDz7cx8MjOfwMyb3GUfZuZb3dfMzL/PzOuZ+QxmvknZ9nPMfKL79/kk29kLNk1oRmecXCxwYJCjM6eOlZWVuOSES3DzT27Gtn3bPIIA2vVoWte2tPHZslVGPagg3No+8oaqFCveTd6RjTGpL+c1SAU2/RDjd1ovBtCW7+tXrMdi2SGEpUE29GmQAFCbMJi6CWPjou93PjmuNY+NT/6T1oOQo9vTl4vfdct3z8T00ys7FYdFWg9i5oQaNiz6lpEcAGAtluIj7jwt3WCiUqwYQ3obt39WH6at/5t47V47WgUhO2Y0ULbKmNkyg2dqz6Du1Ntkc/+HQ0upyGN3I4hKUXTcYQThqZ24BNFoiOtJaYdUXToPAo6DWkn8eJ6iG1eCOJwhR2cF14xbewjYbF86fOMyLqQ0d+qoWBUcM3UM9jy3B1W7ihseviFyiKdslVHL+RWEVATyQi9bZe/G6cjGqOvLQw9MgbmlFl6SFyUZHnn3I9hxzQ6cuOxE1EyVMhVs2nEiyo7/9ijmRJtrE6OpxbQrX9Uvd57xKQJ1HgQAb6RZlcTW4VloRugANv7SPGpk/p7lQhmb+FW4cId4f+vVt3YMJsqFMmqybESAhHY19JmHu0TWO6ruHATtqFxexywIYuPdG+Gwf51acy60lErNJcnICkLXYct2SLWTb4kqtc2IMwJs2/NCJBGVLKEgTCGm6qTocxrNBpxiAhNMFWQE0StsG9NbgOXlFbByFnZ8toLpuZNH3arukCMeu4rdz+7GDQ/f4H10oH4gvICdAhFiagYUhJ8gKlalLYOD2RjbjkG56Q//BDNE+oLsHN0sJinfy5bSYYXcWNN7V2DzT0703q9dshbvOOsdYp8jIog1tr4jW5M/0qcsgjOpq7YkCNLOm6gZajHtWmxuy9pDwOZL/xbTjVNQLYud6WL5lWIF1ZarIgPHWGMt138ftzRHzSMIs4KouyGmXkqpSI9JdswmyO9VNYzogTaZVfNN3/KusG1UA2a5F2Iqaq4z2xYz313USglMMFWQEUSvcEdDVbsKuyXiguMwk1rm0tfsGr7/xPc7Z4qGFbBTIBRE0+9B5JuwchasvOitKsWKd5MHz830ziXYvPusdjbG3AQ2bztpcApM3riSINxOoFwoo8btNhth23jTAZEGuelVm7Djmh24+AQRQzdm/SSMTXvX658EV7lSa1LDtoF8HlVbKA+P2BSDVqyvURCtFtY8q2/HYkwIT+GkNwKNRpsgNB2tUJFzIkc9oCA2rXirJkxbwCaIHBiv3epAREKGmFrzKFvl+JMZHQfVgn9AY0JbQYSEuiSZ5WIShDILviPEpBuI2DaqpTZBVMuZgkgnbBucI8w2ZgEk/0MNDI6DZrGA+eY8npt/TrtKFB/AIwjbFnKaGbWcP7WxXCh7o1fdjXX17DoAwOKJxdjxg5dj+uf6tMyeIBVEXhw3roKA44gRd2BbAKgWMRIFMb3vGHx8S3u+6NpDwOYVv4HpwtmoW+J8A4qCcLOQZKy6VkTHxLpCriCeCxH0IGwbm+4Giuicz3Bufq23DhoNr8PSKgirAgZjXqNSpsu/gM23ARa5M9afJWyeuwTTznpvEAO4YRv3GlPbB4gZ+tIUDz5TupybMCtSJesuqgcR5oV4Ybyc41veFbaNqsUo5ote1p8MMWkVhON4ngUA1CYzgkgnbBtzJcurcFotjQlB2La4qCDKGesQxQcoFUoiPq2EMmrU9I0iK8WKGAWS3tyrWwQGY7YxCy4MODNIEgQ1MZGfQD4nOrGyVTYa58H2zU74Y8OyA9TGoocB28ZlB8XM3fOXnyVG8ZPneaZz2SqhVCj5TOqmlcec45q9ktiUENORpSPFcyGC1677lLqr8mcBaBvQy0rLsMo6wmsPGg0vJm5SEIAbngmSkHuMYysvAABsmVmM6doJgOOgVch7BOF1umpcX8keKhVKmD5jGp+8vF0ibu0hYPMJ7w0tpRIMiZrgKYhCiIKQZNajglDJ1acggr+LkvUEYCpLfAAAIABJREFUQJBzRhAphG2jWm7r9KqpImba4DheSOD1p7w+8mNGgyhbZdTgiBvBvfFrOcevIKyyuHF0lVodR3RYAFrcwrw14Li+50HYvo6rbJVhswM7h/Dfy7a99gUVhDabZRhoNDA7KW7Z2aYSunMVQckqoVSYbJfvtm3USso1qoSepIJYVlomFISmIwKA9ZYIs1U/VMWOa3ZgZWUlqtK4dmdxewRh8CAA+GfdB47hZQBJFe44mJtsx8y0nW4gxATA84iuPeE3BXkue4XmJLaPXXUJImweBKCQnM4LCYbxyPZ9t66wbVQLLd99I5VQ3eRBFNthxqQTJjKC6BXBWGBpNKmPsaGYXBced2HX+Q4mlK0yavJmqLs3OPkJomJV4LQcNIoadeA4QrK7GHjYRkp/2L4OQLbP+AwEtX2B7JJ2qKHLtknBtr2wly8H3lUEpUK5Q0H4rtEitwnF7X+XlZaJ50JoQkwAMJtzkKOcN6qtFCuoQukE5+fbefwaBeEZvEVoFQQAzLqd62yp4IXGfEasW1TQd85tGwyRPSR/03wuLzyPCDPlhYJoopAreJ6ZCZ5yLGiyqQIqrYaYIaZGA7UC+86dPNdzlmYgYtuoTbQJIumBqeYhwhkiwbZFWMlF1VQRM21QOo2yVcab1vc2TV8oCHGTUs2V1+SgbLXDVt7osWyhqLmxZpWZyrNF4MhEQkydCgIQI9rF3RSES2BTxanAtpqR5DBg217Ya1bNgW80UC8KBdHips+DqCodbVWmTQYUxI9zGhNYEgQJgpXPa54qTqGKvb5jVycIBOrwAIBAiqiGhFrUJrtZOcgKEpsciAQUhJ3vLOlRsSqYNT2NTYXjoJZrdQ0vqd9BO8fBcVAvWQDcpBWKSRC2jWreH+bK5/KwcpaY4a4bWKkKYjLZgWmmIHpF8OZL+IcaGDQzN3uBvKDnCgAkQRhG69XJCApi0HF9SRCGNmlDHsH2yTTIoEmti0UPA0rYy1MQslhfMSdCTLJGlqssfB1todUOPakhJjITRJUcjyABtwNWR+guQZStskciKkJVV2DCnufjOY7nkwEidAmggyCC2T+AILDZlj5zzgOzSxBOJILwBjr5pv46Vs5xDRoyC4Mt5g8F21GySmYFoQieasKTNjOC6BW2LcJKLqoT+tmoqYPjePK9W/53GHwdrUIQwdEcYCBPJc0QMGSI9APZwXHDqCC6mtSWP0++HWoYoYJwO4e6U/c9e7pmiRF8ySq1Z0YHMl6qMkQSMKnncy20bEOICQ0/Qagzo6UHUTRfS77ML42CmFXmS85OtNtdnVTaret0le/QQRDdkhBcs7uacyINkmTIp5bTK4jahELCrpKIE2LStWOyMCl+R12aqzKwStoPywiiV9i2CCu5qI4oNz42lDh2lNGTCTqCqLqzWjvW0SkI28asUotpVpeL3w+kB8GN3hSEbXcoCJl+WCtosn6GgUCHWlXTVouEklUSJVCK7Qlxakdbk9lKSprrkWWRFTXXMhOELzPNqqDaCigIy6xGfZlfuowc9fvIeLpt+1M5Vc9DIkRBmCbmqdsCwtuIcg/kKCe8jZyrINR02yCZcUyCsO2O9HBAGNXaUhtBBSGvgYSQEUSvUDpaIPkfamCwbdSKgwsx+RWEnyC88MKkPp/bryBi1rDpBk9BzJsVRNcQk5vl4m6foxxKhVL8ejuDQmD06CkzdzRdtlyTWlEQvpTIfMu3fp7yWFQUlUTrbDCQMd8RYtIShEFB+EJMGpN6ttwOJc1OoEP5CM9D70HoCKJSrGDWMHtf3RZolwqPAjHvx91fSykyGTTUpYKI2hc0GqiS03H+JguT+mJ9buhTzplIetJmRhC9wrYxO9kmiNlxIQjHQVUU9xy4gqgFRuve6FEXJ3WcdlkCGFII+4EkiNZ87wpCkwbpVbEdVZqrauzLtFCZ5uqFmNA2qdURuoyhuyZ1qTDZVkXoHN0DYkSsEkTHCL3RQLXQMg42Qs+3bXsp14AST1cmKS4vL2/PfDcQhPwOsn2zhvpf6rbyO0e9BypWRW9AK21dVlrWJs84CoIcb5KjRMkqGWsx1QqMZSUxqTTpjLqMIHqFbfuyCUaW+hgXqoIYtAfR0oeYqoZsjFmFIGZ1KYT9wCOIud5N6nxTZOcoHZAv1DBsuDnzEp6p22igXhAPM/JCEzLE5F6jE/kJ0W7FpC67abFA++ly6rEAYLY11+FB2OygkUfbgyiwsaP1+VA6BaEYvLNFbisIt90ryiv0xq8SaunwIJyq7zvoziPgJjBEvAd8CiIwYU96ECvKK/RkZkKzCbRaqGkURKlQwpzO63IHLhWr0n5oV0YQKYRy8xFISP9xIAgle2iQCqJJwBwbQkzByTwtUfGyqjwOtFoYcKcrPYjWXM8mdTUvYsM5at8mlWKlXWJk2LBtQaQuZkt5TynU8+xOlAvMg3AVxFGVo8SMZHdio5g3MdmeF0L6UhazAYL1Fa6TCiLf6m5S6/L1g6niFnUQ24rKCn9IS8LkQVhTqNqDVxBlq4yqbo6DoiBEWyPM0pfwiKrR0Y7JwiTqec1+bBu1gjjfFauSeEZdRhC9wrZRtQQ5LCstEz/UOBCEm/ECQJu3HhVBgqhrblbjzGPZeeccr1hfVZch0g9sW5BWq3eTejbXObLzalCNKotJVV2yzEKjIQhChphkbSXltz6qcpQIkTAD8/PuzOtyu7R08HwoIbqgggD8Bnk13zSGmKy8BStniTCjTkEoBu+spSgI93oSo3JN2CbMg7C7KAiPIDo7ZhMqxUp7YmiQIIpKW+OEmGzbe7Jf8PyJEJM+a6rqmtodT3VMABlB9ArXMKwUKyIuO6rMlrhwFYQpbz0qggThPb6xqBltBv0Z96KfJQdLJ5ciT3nM5p2BE4SuTZEIwq3nX811dnwVq9IeiQ8btt2uS4S2Sd20G2goCsLrNNzfOkc5LCstaz/boVoVCsJdH1AqwCrHAkRJj6BJDSimc6OhPU8qylZZn+pst+fkEAizVstrtwwxLS8v18f1bVs7KJkqTqHmCEXbVUGwHTlRQygIQ6hLCYd5lWuj9AVuinDwOwCuSZ3Th5hqboipUqwk3u9kBNErJEHIH0pX8CyNUNrdD0wEoQ0xBctoSAVBNqaKU+L86VII+4Fi0GoVRNgEI699BgVBzmDbGhW2jdlc0zMoZ92wjSzZXiqINNdGntFszHdeo3IE7Co+VUF0zHR2R7fzAQUmycKnIDRKS0WlWBGdqFZBKEQQIDYrZ2HJxJJ27ayIE+W879PNg+D56ArCqui9EKf96NIVlRXtyrURFYRuEAO4aa6G2k9Vd4KflzCREUQKoSgIEQscE4JwnI7aL73ARxDVqvb5vlbOQp7y4oE0WoJw2udPjm7VFMJ+oNy4vjapIY8uI8wqdY4wy1a53daoTw0bBJi9zmFlZSUAiM7VtlF3J4aJmdRuqejmnGtqK9eo7ODqddSKhHJRMak1ISZJsNoQk+pBUPiEs4pV0c8TstulQ46qHCXCZzI05qbtGhWfkgqrI4jQ2l6OgxaJ1N5Yaa6mbCr3PC0viwcgRa4K4KYIB78D4JrUhifpyfkbFauSeMp1RhC9wrZRzSujs1EZl3HhZkH0Y1AD0RQEEYlzEzTwvRCTGJ1OFacwm4tZoqAbVAWhUwERFYTaOcp9xS6nMAh458zGyilBELLTrbuT3OQ8CACoO3PeXA7Z0XqTuKSCKJT8j7dUz4fSeelCTDKtu+HMwyGzSS3bpa3F1GigWiRMFiaxeGJxW4U7DqqWa8Sq1WCDoR2XIFQvzWtf2PNZHEfMMUD0RI2KVdFPgnMcVAsiJORTL3EVhG4mtWHmtqwvJhREsv1ORhC9otFAtdBsj87GhSCkgugzxOSVHwh6EJqYfUdpCtkBQwkx6QzAfuAmEejaVLbK4ROMlOySDnIplPVmZdKQngDZWFFeIWL2bmmKelPE6H0dfnPelxIpFIRKEO2Z14BeQcyGKQi3dIbcZ6iCKFb0E0ndY0wVp8QgIe+0TWo3ddaYdeYqCPUJhmpbZ0shzxdR1GUcD8I8H4O9cwzEqEwc4kGUrJKosqtVEGLexDAGphlB9ApblIqQCmI254wHQdi2l77ZD3KUQyk/Gaog5PuOVDxJEG6dJNF5aVII+0E3BRFGEHK0jkYn4RUrqLKm9EPSUEhrUXGROK9up153TVyf6dya9+ZNyJF4leeFgaoqCNWkDngQs5rzF0xzlWGrbgqiKjOUAt9JlunwdXaO01bnpk7XNbLVOSpAdIIwXa+h30Gevw4FweFqxwTDXA5AmtQtsKNcYzJ5AoqCyAgipXDjwe0Yuj0eBOGIMsf9ehAAUC6UPIIwjYS0mRZeBzzfDjHFLXLWDSGjREEQiKYgdNui0dlRJA2pINzieVPFKTGxzLZRdwnLpyBa8+5v3fQ62pY0UGs1dx5EyW9Sx/EgimI/UUbinooMnu9GA7MWt79Pzml7EPlWp4IItK9WpI7rzfMgwqorh3TMxu9QrOgNaE1bI0+aVcJ4OpMaAOZb/u/cImDOLRFSLpRFv5N5ECmEbXvpfZ7JOg4EoWRB9IuynLXbRUF0lKZQFIQXYho0QYRkiIiYeEgVzIDCUeHraEdAEJ7qKlZEZVfb9uoo+RVEo32NBke39TrqbghnIj8BAkUOMXkdsExOMKg0FWKQYFIQLUEQ1lR7kOWIh/mo7e4wfl0FoRuQAMkoCFM7ZKmR2CGmEKLykg3Y1q4vVVct4X4nI4heIRWElMdoeFPnUw1lVNkvPPPRMA8CcM29YB19rwOe74yPJxFi0qmAsFGe44CV9gW3BUbwXOpGA408YKPpjbilMqujU0HUYHuTqmRZBkB0cFydRc2dN0FEmMwVtfMgZkPShKtFALOzkRREuWBIx2w0RJhWEh7sdg0pnYIIXEN1y6wgZifCQ4hRiE2F8XnkjtO9rSZ0MakBeL+tPJZKKDLluqNU+wCREUSvsG0vy2WqOAWbWt2fc5wGDFJBaAhCN6LrqKMvQ0xuGYyp4lT7ITRJmNQ6BRE2A9W2MV8AWuCOLKbIM7EHjYDpPlWcEmU31BCTajpzwz+IUUa39lwNrVw7jFHOTbQL/CnH0ykIWdF2djInCCKqgsi39Aqi0PLuoRqJEArm570S2MZO17ZRszprQLUJIjyNuWcFofNC8q1wtWNCmEmtq5EVuKblb+pTGQNGRhA9gu2GZxYZZwynEY7TNW89KlSCkBdusHxH2SoLBRHwIJwc0GBBsMYUwn6gjhJ1cxnCamep/kVQEcXtBAaFQIftzb1pNLwZ0j7TGY47iBH1htR2193HlUq1UcpNRE5zBdwOfzLnm//SdSZ1vgkOjnQbDczmmz4C864n6iS2jnkQVmfH6oXAwhREjx6E176gguhGZiaEhEG951IjSIrtdvvCXgnNyckIokfMt2y0qD0PAhiPiq4tu4F6jDr4YSgXKz4FEUw5BAylKQIGcqVYQZ0b4eUR4sIdbeUpj2K+6PtI+CLhCkIXXpHbAiMIMQUIQqSFCuKtS4JQJsrV2QY7tnYQU2+4BOGSSSk/ofcgJtrHU+FNfIvqQVgVNIlh2/Od3ynXVuGAO7+iVvMeBxqa5qqpIlsqlEQKcJeJcr0qCG07cs1OMosxUY5AmMhP+L+HZVAQynUZO2uqByRKEER0GRH9lIgeJaIPaD5/BxHtI6IH3b/fUj5rKstvTbKdsdFqtZ82NmYKQnYmA8liChCEbhTpK00h4Ti+FMrYE4yiwBZPU6sUKx01p0SIKWQGakiMOnYnMCgE0nYrxUqbINxOxDdRjhzYTRtNzSCm7lY79RREYVJbamN2Mqcl2KnilBihz84aY+gqvGO3AgSh1HHyDbLm50UJ7ODyDoLo7ODl5MzZCURTiBGVtC+9NxDqqurILEo/4CqCSqHUcY3K33GOAt9ZpyAS7HcK3VfpDUSUB3A9gIsB7AFwHxHdysxbA6v+MzO/S7OLOjOflVT7+oKBycdBQchZwAMJMSkEUS0CZauzOqw2wyuQQtlsuc8ILgKLBnX+bPHEPyNphdWw6ZIiC4zGg/ApCGvKmzdQz4nEiFKhhBaL13XY2kFMzYIXkpLfpVSY1JrU1YkcpjQEKya+uQpior3MBO+coYEjlOVsNzDr1uNSFYSTAxokjF/fhMygB1HQVwSYKk5h1gpJ/xy0gnDJTJZxqYYdW4XrQZTznfeNZ1KTEjoKehANRUEkdC0mqSDOA/AoM29n5gaAmwC8LsHjDQ+BSUTB8gOpRatlrP3SC7z5BK2WGw/u7CQqxYow8Fv++HEwxAQMOK7viDr9uo5LEkRHTFzCMAdAbguMJsTUYVKT7U16IxCK+aLPg1AHMepoU1ZB9UJM6kOGlOPNTuY6vr/cX7UIf4ipyzwIQGSFqZhr2WDqVOFq5+095lXnQbglzoOYKk6FX0s9eBAmk5od2/foUs+Xi+FBVDRt8EJM5HSsL48zjMFKkgSxCsBu5f0ed1kQv0JEDxPRV4noWGX5JBHdT0TfI6LX6w5ARBvcde7ft2/fAJveBYGbtaNGfloRYor1gpKcKAdxkZo6YwCooXuIKTRuHBe2eOiMSUEAwFzTQBBpNKkbDb9JXaygnmuiWZsVk95QECmrcuRZgH+0qYaY3LiB7ITKVlmf5mogWG8OhmtST5CFfC7fsZ6Edw0Esm3k5Migggie+4pV0Y/cDTXFhIIIV4g1CyhQocMzM8EU75+D45GcXK86ETLHRoXbj5Q1JOeZ1OR//rUxcjGGHoTuYQPB+si3AVjHzC8C8B8Avqh8toaZzwHwNgCfIKITOnbGvJmZz2Hmc1asWDGodndHMB4cfMpWWqEQ28AUhNvZ6CYtAcroMee/udUReiJxfUkQYaQVjIkr26bZpPb5Nk334T8kPiQilGChZsGXQukzqU0KIuhBTHQqKMBVEBZ7aa4VTYjEt77syALPvZb1t1SCqFroGN3rRuVsN1A3EETFqoiHD3XxmHQjdxNME+VkZV95fitWpfMBWSY0GsaBlacE8612WXmDBzGuCmIPAFURrAbwhLoCM+9n9nTnZwG8RPnsCff/dgD3ADg7wbbGw7gqCOUCG1Sa63wBaJKoLKq9WeXISz7NzG1H4iEmu13nR9duQMTEtYhqUg97HoSGVGeLQhGUqD0SLsFC3YLvHKsdXFBBlHQTBxsNVC3SEoSYpMeegqh0eTKhd+ycsn9mESKD/xqYVUJMXqdbrHRcG9KANyqILh5TzTByN8E0UU56eiqZaUub6+D+prrQrKcgCminsBrmQSR5LSZJEPcBOImIjiOiIoCrAPiykYjoGOXtlQC2ucuPIKIJ9/VyABcACJrbo0PgZvWVH0gzQSjtHpSCAMSItFbU79M38pKzzIcRYnIc73kdpjZ51Tm127rtS6GC8F1zbocqFQQgyKJegC+cmM/lMZmf8CkIz6QuGkJMxc6JggDaczDc66kbQXidqxpmbDZ98yx8IabANaqrnSV/OyNBhD3j3A21xrkHrLyFAhU6VK5UxmqIKa6CKBdDPAi1pIsSIpYPhwKSVRCJZTExs0NE7wJwJ4A8gM8x84+J6DoA9zPzrQDeQ0RXAnAAHADwDnfzUwF8hohaECT2MU320+gwAiYfCEJi671AvUBNUrkjfJTPd4SY1CymoSoI0wzUQAhRRTFfRA451KxkH9SibZMbNy/mi74OtW6JyW4SpVxRKIhAmKxSKKNqzfs6GcAlCJ1JbTGOMyQeVPMtUY7EEvsNg6cQc02hIol8nkqYSS3/VwOdbjeCCH3Co3sfxB0kVawyatZz7XZokj5EWxHLg6hMLOr4yPcgJ4UgqkVgMldEPpcfigeRGEEAADPfDuD2wLIPK68/COCDmu3uBXBGkm3rCwGzKHYVx1Ghh+yNMAQJoqzpKDpGORMTHSN0p+XWZhp4iEn/3Iv2iDa8AyFQR5YMEaFSKKFqVUeiIKbc8xk0ncv5AEEETGpAVt892GlST0xpy31XC2YF4eQYjbzo0LsNNjpUl2V1qHC5jtakLlawPwZBVCx3jkiXNNe4gyRx/hSC0IRsK1YFT1sAGnEURGc7Jgri95xTFYQMjeVF+GncPYjDFwEFkc/lMZkrjoeCiJCWGBXqBVrVzGoFDFk/SoipZJWSy2LK68uae+2mcAVRKZQ75gAA4gYd+jwId8QtR+sdCsLtNAC3dIZOQbij2w6TulhBowDxHGsJ28asWycpCNVzM6k03/o6ha0qCEvcQ6XchNGkDo7K5W9nDDEFZ++r8Mp0xCeI4HWsUxCx01w15zhHOUyg0BFiqlrtpIBCroCiDHtlBJEiuBNc1BFmJV8aKwUx8BCTiSB05p7MIimIPPckspjYsb0yzMZ2k7kDmQ2JrVcKMcIIg4JLWlPKSBVQTGqfgpjQKgiRfaQxqSdEB1V3ar7jzRrOn+q5CQXR2cGp8I10pUqxO4sBThXKPg/ClxkU8EhqyjMwdO2bz7XgOOZ5LqbYfxg60m1VBeE7xyEZVIF2VA3eHQBMkuVXELLdyncu55IdrGQE0Qvci7ucm/BGmJVCKf0T5ZQRj+7Gigt5YT9fBOYL4fMgfB2q2w6ZZpjP5TGRKw40xDTXnPflp+vaZCSIEP9CbF8amUntdaaKSd2hIPKTPgVRVsJScn2LcyjkBFOU3I5SluAAgIYzDztnDjHJY4sYejhBFPNF5JHzD6A0D8upuBPioqS5BmeDq/DOTVgIMQKxBVEuBgYGmqQPkfodUudLQashUpRNg7USFbUehOr5VPKT4+tBHLbwpF77pvRGlWkmCHcEMknF0IlNUSFviv1l/3sV2jIkcoSurD9lVTBbbAzsQpd1f3pVEGF58pVCl+dJJAF5zbnn0xdiKsD36M1yYRK73VBNkQoeEVSKU9jnLi9BSYuVhqhCEPL8hYaYpILoQhBEhAoVUS3OaRWE/I2mChV/mqt7nGXFZbj2lR/HtqmzgW3bAABL/+QG3FFqYcnzS7DNXSZxQfkC3HHJHdh1QR6FwGcAgMsvxyf5ApQmpzq2DcOfnv1n4PVz2FZYKtrRbOKFf3UH7igBc0/OYdtT2/DWo9+Ky197ObbZZa+tJrTe/GbckbscS0tLte244eIvY+IVLWw7eBB4/nnghBPwvt+5AzxR9Nb/woU3onhBE9umFnc93uTkJFavXg3LskLXU5ERRC+QTK5MEKrI+GSw5n2a4HZ85dxE93UjQHa0+0IIQpsW6rZjylIeZVkooVo8OECCmBP7DVMQOUOJZKlwDCM7US4cI1EQRxVFxovPpLb8ilDWVqpaQCWnDGImprBDEopy63splU6bIGZDzl+Hgphc3LX55dwEatZcm1Tde2iC2hWA///2zjzOjqrM+9+n7tJ9l6SzQiQhCxDCTjAJwoxCcEHCFgkj4IuvrAZRCUEdPsiAo2MYhGEgosKwiMIYm10g+AYYoTMqGCQEhQgIwSAJCQkEEtK393vP+0fVqa5bt6ru0rc7Sff5fj796Xurazmnqvr8zvM85zwn67jAtGWh5wIcNfIoRjSNYFp8N6y99gLgfTooNOWZNnZaybrUW9q2YG21mPp+nMb99y8tzLp1dMomRg3bjYlNE8uWXRPfEqe7dRv7J8fDRz4CXV28E+vAaoL9xu1H3IqzYfsGNmzfwLS2EVj77BN5vu6//432xPtMbJrIbpndSv6e39hFqq2bvcdPhcZGeO89aIVYJsu+Y/cDQL2TJ9HWwdTRe0NTU+i1lFJs2bKF9evXM2XKlIrrbFxMtaB7c55/Sjc/zS5gQXgtn76gG9r3IgSiIdaAhQS7mDyNj/aP100gIpIS9gpEyOp/2t8f4oJIJ6sIRNYLRyB0b9076qctUWxB6JnR/k6MTq5oz7zu7UW681l6OtxtrZS3ID5sgI6QYZolxzgBaLcDpYPuns5KJtlrQaQkiSV289QUbyKeieNNxFAQ+7Pex4u2jvMliRsclKIgwcdGERPLXtBIT/h0zmP/LVb0u0D5lSULKrwOABbB1/Pub4kU7xOCiDB69Gg6Ojoi9ystg6F6gnyBuoHbmQVCj//uJ4EIaoxFhIzVGOxi8q51nMjUdRSTTgwX1AN2M4TGVfASsa6ABQtEJpHdcUHqRrsxtsQiLQ2BLqZUPNVrQXjdoE4SO3vmdbJof4C2fG/joRdwiopBaMuxnIsJAoKpTicr6xGwbDLrBqm9wmGJBWKv8KfRn4MaV72tECIQitoEwrJi9pol3gbbsger6FhkuWt70Zl3QwVCN/5uwXW5Y559HBGpYKnjoBF55TACUQv65fY0iNlkdpewICrJnVMplVgQoN0LlLqYPA2LDqDWz4KwG7gw0Uo7+YoCn5evt+7HTXO+A4a5ZpO9vfVsrJEPG6ArDilPPVOJdK8F4e/EOEHqtFcgtIsp77UgwgVCb9uc6T1vOTKxRvv5+i0Ir5vWeQfaEragaHTDphtUwO2fRwqEBDfS5XruYVh+CwLsBtuTds69dpkePfRaGWHlEAQVYEHEiiwIq1hE6owRiFrQDa2nQcw07BoWhD1Mrr4WxLuZ4u9+MlZjiIvJIxD95WIKiyOQDG/kdZA6RCAyDdkdlu7b2xhnrVTvAIEGr0Ck6IxTMlQ3k8y4OZpSVqkF0e5JXujNk+RH31NXICoYMu3G6HwxiKynfNmGYa6LyWv5WE4zla/ExTRuHNmGYcwcP4umg2bas7ZFYNy43mPL9NzDePzRx/nonrN4dc0ae4NSPPPH57n4rIW9ZRWL7y78Lg88tsypZjeXXXYZU6dO5aCDDuLwww9n2bJlTjl667B48WLa2tqKrlciSEqRF9uS0fzwmh/xzNPPlnUx1YoRiFoI+GfNJIftGhZEgrKpESolGUtiKSlrQWT8E8uCXExO41A3gfBl2fSTljICkbBdSYHHNtRXzCqhq7uDrnhxjz4Ta3TdPClPI60/b0kVj5nX92JL2pcgzpIuAAAeqklEQVSaw7UgegdYeDOt+tHnqcaCSMdSxfMgfBP/wI5l6CC11w3qWhB4etLY6aJL3CabNgUXwLO9XM89jEceeITph0+n+eGH3XLkKU5brc+pm+srr7ySjRs3snr1alavXs3SpUvZvn17STkCBQLbxZR33jMVEIP45re/weGf+Fi/CYQZxVQL2oLw/rM2ZHcZC2J8HeZAgOOqUXHeS5fprfvHamsXk/f+Jevr19cNXGiZrGSoFaC6u8ilooLUWToSUOjpGrAeVs6ZxOatTzaedq23lMeC0NbEe2nYt6gT07t9imdinRuk9loQThK6oHugBWVTtvi8UehZ3N5hrrkE7BH31KdxuD0yKukTCMSOG6gCLFwIf/oTY9q30xQDkuUD5C6zZwMQ72hnmtVDYzwFVhymT4fFiyMPbW1tZeWKlfz4nh/zL+dcyvduuAGUQoldPo03BtHW1sZtt93G2rVraWiw7/fuu+/OaaedBkDeadT/6yf/xYYNGzjmmGMYM2YMLS0tZLNZzr3gLFqe+l9uvuZ6nlqxgqUPPsj7Hds58sgj+e87/hsR4Z8vupTDj57FofPOZfLkyZx11lksXbqU7u5u7rvvPvbbb7/K708AxoKoBddH7fnnaxxGVxy6u6obJTCguK6xvs+i1qSJs8XRm1ALQq8IpntC3V2lFlidBba8BdEQakF09HSETrKD3o5Bm2feQH+Tc4agFolqPOVabymPOyzlEQKvG9QbMyqaea1dTJ7stq0Ra5dbYpFWieosCP/cEW1BeGYzZxqHocQpt6cTExb4lcAlZyqnmqMfeughjvnMMUzaexIjm0awatWqolFM/rIqFGvWrGHixIkMHx48DFjX56KLLmKPPfagpaWFlpYWAHK5HPvuvy+/eOTnfPzII/n617/OH369lHueuofOjk4effRRtxYFcC2IMWPGsGrVKi688EKuu+66KmoYjLEgakA5s0Aznt6LHgue62ljxI4qWDlqTFIWRZoEm5N24xUapI6l+MDTW+/o6aRg+XrD9XYxWeENHHgC5wECoRvj8JnUzjDZnnaqm4tbO62F0jJlExned9rRIoFotD/3xErjPADdMYrWQdYWgTe7bWss3IIAyEqSzZloK82Lu6aDb6Kcdy6MDsBvzsA0j0AUuZgWL4Z8ng1rXyCXjnPwHr5l66NG6ixfbtdz7av8taGVfUfvy/CG8nM4AJqbmznnK+cA8PmTTqC5uZmPfuc74LMg9DBXVcGIoaiRWLFYjBNOnMPWng9BKVpaWrhm0SLe79hObluOGYfO4KSTTrID5NIbV5k3bx4AM2bM4MEHH6yoblEYgaiBzu52u4FrKHaRAOS6d2KBcMz6emRy1aQ9M3JDLYhEcYAylw/oDSez9MSgq6uTZNBJqiRn5REVnlIkHWvgwxCBaHXKF25BOPml8gNnLeacOQpZ39Bg5bRDqZRHIHyjw4I+pzwDFXrXsfbEIKw8CWWRjAU/jYw0sC6dsz9XYkH4FyXSQWrPHApdty2p4tiEboCLYhC+0UPVUO0opi1btvDUU0/x4ksvkld5rLzCiie49oorGD6qiQ+3fejua4nFh1s/ZOTIJvbZZx/eeusttm/fzrBhpa6wKIFobGwkEU9QyENHeztf/epX+f3SX7FtQpJ7f3Jv73wG3wgv7cqKxWL01KGzZVxMNZDrdv4xGj0WhJ7Z2tMWeMzOgIrIHlkr3uGS4S6m4oll7ixn3zBh6O2995VcLE+aROjYbzcja8A/UVD5io7V+aUGUCD0zOZsgEUAkPI0tEUC0RCyv0cgkrEkoqDda0HEC2QjpDojSXqcwTSVdDgyySxdcejptJ+v6ux0XEylAlawis8ZGKQWe55ACbvvHlwAz/aohjmI+++/ny996Uusfm01jzz7CK8+879MmTKF3//hD+w5ZSKb39nspr5Yt24dr7/8Ogfsvy/pdJrzzjuPBQsW0OVYThs3buQXv/hFSTmGDRvmBq81ltjDXNsdMRg1cgRtuTaWPrS0995QLBD1xghEDeS6nYBhqndqu5t+ID9wfulq0ZZPUP75WtECkfQkfyvZx5euuTVfmsbBXXKyTvcvFyuQ8Vg3JWWKSNmdCyhf0bHaxTSAz7pVlZbJKxbarQSQSvWKRTrUgih24aQKMdr1im/5vN14S4RAeFN4VOJicoQq19UKQFd3O3nLDkwH1ScdFYNwJqhZQc3XO++AUqx++0+8seY56Oy0/fPvvOPuUq1ANDc3c8oppxSV49RTT+WX991HvDHJDTf9J+eccw7Tp0/njNPO4IrrriA73H4GixYtYuzYsRxwwAEcdNBBfO5zn2Ps2LHueUTZ93/+/PnMmTOHY445xr2uONcbPnwYX/7yl5n1mTl869xvMWPGDM8+Tp36SSCMi6kGcj1tIMEvd716wP2B/uesZOZrpaStBihAWoW/Sv5Z5kE99HoLrC0Q4Q2cvfgLwQKhwtNMgKesnlE//U1Q8jyve8YrBF6x8KbByPgm03lJF2K06+SFeiIo4Tm7ilJkVOJict65ts5WmoDWTv0uBpfPa+W6vWQpnqCWiHAxRaWgqFYgljuxi1xXzj1+wYIFsHUrq1rXcPgRs1ixYgVgD0V9fuPzFNrsaySTSa699lquvfba0nKIct1kF110ERdddJH7t9bWVjZ9sN45Z4FFixbxz/PP4vX4NvYbs5/7Htx028288cEbFFSBN9980z1+5syZbrn7grEgasANYiZLfb268dsZaXPKna63QBAtEOlk2nYvdNuNXFAD7ApsPe5foWCP1oroAbvj8oMEIiITLHgsiAEUiNaAmeFeoS9K1ud1K3ncoOmAEU3uMSrWu2a0DiBHJHXUKTJiSkLjFF50OXQjq9202VSIBZEsdTG5qRUryKXk5jEKIF+lQLjn9M2SDpqXICJYSkJncXspoIhFipy+XqHot+WbSe39W70xAlEDvQIR1APeeQXCdY01VjF2vAw6JUKUO6d3WKh9/daAPEmui6keAqEnBEp4A5dOpEInNkblcfJub1MDl7k3KDeS14ItSvftva8NpXEye3+/QMRp19lt3UR64TPudYqMTCFWUY6ftFOONkcgWrU166lD8ftQ3ImJKSmZKBcVpI6JVZw3yUO1FoSm18VUcIpRAF/qC4CYHnpahjwqsg7iF4iAcleT+6kWjEDUgDtpKWASkm5cdkb6xYJwgp3pqICmO8LL6T2qgN5wPd02er5HlA89kaE7FjxvpTWgfF7cIPUAWoutUioQ3sa/ON23Z/a0p4de5GLyraaWIk67OALhWhARAqFXUixU5qXW5dD/O/p31hPHK7Ygiu+9Pas4PKupHwuhYBEqEKL6IhD2Od0evVV8HgshX4kFUWYklj6vcgXCWBC7BEFrDfQ2cDuxBdFTGlzvK3rCVToqIKz9z11O4xDUG9YiUg+B1RZERAPnzh52fOFe3ER/YRaEXkZVDdys+Va6Stw5XvdMUTZXz2dvD70x3ohut7wzr8ERCKtXIHLJ4kyrflyBiHAtFu2vBUJbka41GyxgfivX8vbKo0YxOZSk5vbg9f1XQ6hASPHiW1aFFkS5cvhdWkEWhJ53ka/oitVjBKIGWoOCrNpFMoBuh2rR4/Yryd9fKXq0SWRv3bmeFijdGw50MdVDIHSyvQgfuhtH6IoQiHIWBAPoYpIesvlid0620RZ6Ufa6G5pUQP4lcFKvF+wGJeUTvzQJ2vX6GNqCiBQIZxlTVdnqZGlHILQV25p3LIgQF1ja52LyWhCu7z+i+bLKuJhqafjCBaLUgqgsBlHGgqjGxWSS9e08uEHMIAtiJxYIHR+p6zBXpzFKR/j7dW9QNw5Bi/nU9f65S8KGN3D6HrR15kr+lqMbS/WuG1FyrBaXAXzWrVZPSW9dW4KNeSkSDm+5/cFo7RLyuxlTkqAtViwQmYicXTrVfdRIsaLrupkGnHcgXzqvI27FacxbgeXTieug1/cf6WKKsiDK+P7DsAPQuO6jKBdTJSm4KxE573UKqtQ1pq9tYhA7EW4Q09PAxawYDXkZ0F5ltbSVGd9fC+5i7VbEiCE9Bt5xK+ToJl7wuUvchebrJBBJIlfO0wntdNDUS066yeTDg6/6WbcxcC6mnNVDtlDcW9f++1S++N9YRGjsscvuf9ZpbUE0+gUiSXvcCb46qWSyEVl/dVwpynIsuq47d8R+B93Z6j4rTVs4wS6m6J67ZslLSzjirqOZdfvhTLr1AJa8tKTo7wUBS1UvEL/61a+YMWEWr72x1i3H8888zxc+/8Wi/b59yZUsW/YkEJHu2+cmC8rmKk5ab+XUOx9g+Vhisfyx5bz62utV16cSjEDUgO7l+tfCzeRjbhbRnREdH6lrqo24FogoC8LpPTqNQqt0k8lbJb1eUdBaj0a3IguieGSVl1arx22oQo8vDOyzbrXypRaEI7ypQum/cSrvCIS/AXbOkfI1wClJ0h6zG6KuzjZ6YsULYvnRwlOpQPjnueQCZoYDZPP2fU+ninMkeXvlhYIdKwkSiCUvLWH+0vm83fo2CsVb29cxf+n8IpGo1YJobm7msFnTeWTpYwDk3cBw8bns7LOAUuHpvn1usrD1IMBjQYgqETZBbIF4fU3V9akEM1GuBnKqi3SPlLygmULMdZ/sjJQb318L2lUTGRB2Zvbq3mPO6i4Z/SIiZPIWOem7BaEbuKh1L9zAeYBA5KSn7OicTN6iTQZuPYhcLF+S+kI3rukAMUsXYnxAocSC0DGDEgvCStLuVLm1w84tFPWe9FoQ4R2D4v0di815B1sDBnrY5bMLkQkRiIWPLWTV+ufIFTpotJIk4sXXX7F+BZ354jhWW3cb5z18Hrc9f5v9vWM7iLjvwPRx01l8XPl0308//TS3NP+Yi8/5Bj9c3Ntw+w1NAZRAWy4Xnu47n3ctiBtvvLEk3fcTTzzBd75zJVtz29h38hR+2XwfBRQ3Xv0j/tDyR+LxOMceeyzz5s3jd0/8jhf/8AI333IXDzzwAHvvvXdkXarBCEQN5Ogi21P6T5ktxN0sojsj2mdeVwvC+QevxoLISZ5sQKOW7YmRo+/3L9dh57TJRNQzUiCs8gKRLsTdlOIDQWssz5gQgUgF3MtUwQpMVqjnq5RYEFaDLRD5PK3t2+zzVyIQER0DLw2xBkT1dlJyqotEnpJJdllHwPxB6hhiB509vvagdN9+cQjcLsHHRvHQQw9x3HHHsddek2kaMZxVq1Yx6SNNgeUQR8zWvP56eLpvvXwoFgsWLOD666+npaWFMWPG8N5777Fo0SKWPf4Yb+Te4OGf3MP111/P8XOP4qnHlvO3NWsREbZu3cqIESM4+jNHMeeTs7nwwm9WVadKMAJRAznpDnRBZAa40aiWnOokkYdErLKRJ5XgCkSEv1+PYnJ7jyENcKYQo9XquwWW0z3giCBr2g2cBwlEnoyK7hmnC7EBtSBaEwUm+dw5WuhTKkggYqR7pCSOooPK6XTxUGdvbqpcpy2wYalGoDdGEBXn8SIiZHrEjd+1qk4y+VIXUcYVCN88CCfovPizi2n9YBOvdqxnamZPmpqKk/NNXjyZv2/7e8l5JzVNYvnZywFY/dZKUlYDe084uKKyg+1eWrhwIRbCnJOOpbm5mUsvuUAva+erbPl034VCHhUyVHfFihW8/PLLzD56Nu3d7dBd4BOfOJp0Nk1DQ5Lzzz+fE044gRNPPFFfrt+C1KL6aXgUgIgcB/wQiAG3K6V+4Pv72cB/AG87m36slLrd+dtZwBXO9kVKqTujrjVz5ky1cuXK6go4bhxLdtvEv3wK3mqCUU5b8X46+vP2BuiKwaRtcNWTcOZfLJYcWOC8udAZg9EVnifo88RtcPxf4f9Nq65MlXzOJaEj7in35t2LkphVzbhxXHHAJq46GlAh5x03jp+N28S5p9j7jG6DbY3QY5Xev3Pm2msV9OX+jWqDvAVbUzAmB4sfC6jnuHHcMHkT35jTWybv8SXl89VnyW6bBvxZf5Cyg6v+e/Z/59l9av/2c+cGv6PzT4K2JOy5Fa72bP/a8bAtBaNy9v3bloKxrXDD48H379+mbeJfPxnx3H3vyZLdNvGlU+w6jG6z18vujJeW78snQ3sCJm6Ff3e2r3zi18SmjSFvQbxg1zdvQSIPEz6E0T0JOPRQwIlBPHx+USr2xlgjV378co7ddw7xAvQIIJDMw3jf8UFs2bKFCePHM2bUSPIxIZ/PY4nw6LNLef21N7jm0qv5zZ0/ZXS7nap83sJv8H++ciaHHHIgcw4/kYeffZiRqQxNHfa71RWDWMGuA9jlOPGIk1m15JeMmT2bpUuX8subbuJH132fN0fa+8QLkBfo7Orihd8+x28feIJ3N27mvp/fzNnf+y4f//QnmDPnU2Xr88orr7D//vsXbROR55VSM4P27zeBEJEY8BrwGWA98BzwBaXUy559zgZmKqW+7jt2FLASmIn9PjwPzFBKfRB2vVoEYskh4v7D1Eq6C856Ae48rG/nKUJR3XJXNZDugluXwpkv1v78lxwinH8ydHgMEv95y93jfrl/vvP767nkEHEbomqPr8c7U0QNzzrsntVru/9aQfev3HP3Uus7oLfPvXIZoyeNCTzWUjBpK4w+sLd9u2XZ9/nuyzexqXUTu2d356uzvsqcfeZUfLyfW265hWeefJyLb7jcDZTPP3U+F156IQdOP5DPH/15brxrMTPHT2H1+xs5/9T5NP+mmezwLDcuupEPtnzA5ddcTiKZ4L1N7/HH3/+R4089vugaZ3zyDO794X/y0c/O5d133+WwQw7mJ/fdzPi99qSjvYNNGzYxdtxYOto7GDVmFNvf38YpH5/HU395kmuu/A+mHTyNk08/uWx9diaBOBL4rlLqs873bwMopa727HM2wQLxBWC2UuoC5/stwHKlVHPY9WoRiMmXCH+vw+o+sTzkowe97JRM2gpv3lD78w+7f97zVnKP+/v++etZ7XOvtj4DQdg9q9d2L5Xev7D3qS/vQCwPj85ZxpgQgQC7B37Inr3t24vrVtJVxfvkP97P7NmzOf28ecz61D+42+7+6d28+fqbXPaDy/jzc39m8fcW09nZSTwR52uXfY2PHfUxALq7urn52ptpWdZCsiFJKp3igm9dwJGzjyy6xj133MP9P7+PyXtOoaWlhdvuvpkbfvAjurtsl+tXLv0KBx56IN8895t0dXahlOKLF3yRE087kT8/92eu+uerSCQTXHPrNey154TQ+lQrEP0ZgxgPrPN8Xw98LGC/U0XkKGxr4xKl1LqQY8f7DxSR+cB8gIkTJ1ZdwLfqlHEiwJW6S9DX+ocd791eyTX6+/75y1Btvautz0AQds/qtd1Lpfev2u2VlKOS8vnFoBpxqGT/5cuXs3JDcefzjPPOcD8fOutQfvbozwKPTSQTLLhiAQuuWBB5jdPPPZ3Tzz2dmXvY7fRhR83irqPuKtnvzl+XetoPnXUo9y6/1/1ez5lY/fmvGWQ4+7sXS4HJSqlDgN8AuvaVHItS6lal1Eyl1Ey9CEc1TNxW9SGBxPonDUq/09f6hx3v3V7JNfr7/vnLUG29q63PQBB2z+q13Uul96/a7ZWUo5LyJfPR36s9vtZ9+hon9l6jL9ertv5R9KdArAf29HyfAGzw7qCU2qKUm3znNmBGpcfWg6uetP2cfSHdBfNX9v08RfTfuAGXdJdd/74QdP/85y13j/vl/kWUp5IyRR1fj3emiBqeddg9q9d2/z6V3L+o96nWd0BvjwrRWMoONHsZ/6G9vRKCjg+i3DktBWPbKr9uuXLUer1K61Nxuep3qhKeA6aKyBQRSQJnAI94dxCRj3i+ngy84nx+HDhWREaKyEjgWGdbXTlz8+7cutT2nYqC0Tn7p9LPk7bagbmbHrf6dB7/OS/8Y33OVa7cZ24OWb+3xvsXdN6oe9wf96+Selb63Kutz0A867B7Vq/ttdy/cu9TLe+Ad/uoXIGEM6I4XrB/wO4pT9rqjNrxMLonwaStvT1p7zGVHB9E1Dn1eSZtI3Kfsbno473lqOV65epTS7y5v4e5Hg8sxh7meodS6ioR+TdgpVLqERG5GlsYeoD3gQuVUq86x54LXO6c6iqlVLCTz6GmYa4Gg2GnZ+3atQwbNozRo0dXtDiRoRSlFFu2bGH79u1MmTKl6G87ZBTTQGMEwmAYnHR3d7N+/Xo6OnbetVZ2BRobG5kwYQKJRLGFsaNGMRkMBkOfSSQSJb1ew8Cwiw7QNBgMBkN/YwTCYDAYDIEYgTAYDAZDIIMmSC0i7wKlaRwrZwzwXp2Ks6swFOsMQ7PeQ7HOMDTrXW2dJymlAmcaDxqB6CsisjIskj9YGYp1hqFZ76FYZxia9a5nnY2LyWAwGAyBGIEwGAwGQyBGIHq5dUcXYAcwFOsMQ7PeQ7HOMDTrXbc6mxiEwWAwGAIxFoTBYDAYAjECYTAYDIZAhrxAiMhxIvJXEVkjIpft6PL0FyKyp4i0iMgrIvIXEbnY2T5KRP5HRF53fo/c0WWtNyISE5EXRORR5/sUEXnWqfM9Tjr6QYWIjBCR+0XkVeeZHznYn7WIXOK826tFpFlEGgfjsxaRO0Rks4is9mwLfLZic6PTvr0oIh+t5lpDWiBEJAb8BJgDHAB8QUQO2LGl6jd6gG8qpfYHjgC+5tT1MuBJpdRU4Enn+2DjYnrXGgG4BrjBqfMHwHk7pFT9yw+Bx5RS+wGHYtd/0D5rERkPLMBe4/4g7CUGzmBwPuufA8f5toU92znAVOdnPnBzNRca0gIBHA6sUUr9TSnVBdwNzN3BZeoXlFIblVKrnM/bsRuM8dj11Uu93gl8bseUsH8QkQnACcDtzncBPgnc7+wyGOs8HDgK+CmAUqpLKbWVQf6ssbNTp0QkDqSBjQzCZ62U+i32+jlewp7tXOAuZbMCGOFbqC2SoS4Q44F1nu/rnW2DGhGZDBwGPAvsrpTaCLaIALvtuJL1C4uBSwG9uvFoYKtSylmjbFA+872Ad4GfOa6120UkwyB+1kqpt4HrgLewhWEb8DyD/1lrwp5tn9q4oS4QQctTDepxvyKSBR4AFiql6rh67c6HiJwIbFZKPe/dHLDrYHvmceCjwM1KqcOAHIPInRSE43OfC0wB9gAy2O4VP4PtWZejT+/7UBeI9cCenu8TgA07qCz9jogksMVhiVLqQWfzJm1yOr8376jy9QP/CJwsIm9iuw8/iW1RjHDcEDA4n/l6YL1S6lnn+/3YgjGYn/WngbVKqXeVUt3Ag8A/MPiftSbs2fapjRvqAvEcMNUZ6ZDEDmo9soPL1C84vvefAq8opa73/OkR4Czn81nAwwNdtv5CKfVtpdQEpdRk7Gf7lFLqTKAF+Cdnt0FVZwCl1DvAOhGZ5mz6FPAyg/hZY7uWjhCRtPOu6zoP6mftIezZPgJ8yRnNdASwTbuiKmHIz6QWkeOxe5Ux4A6l1FU7uEj9goh8HPgd8BK9/vjLseMQ9wITsf/JPq+U8gfAdnlEZDbwLaXUiSKyF7ZFMQp4AfiiUqpzR5av3ojIdOzAfBL4G3AOdodw0D5rEfkecDr2iL0XgPOx/e2D6lmLSDMwGzut9ybgX4GHCHi2jlj+GHvUUxtwjlJqZcXXGuoCYTAYDIZghrqLyWAwGAwhGIEwGAwGQyBGIAwGg8EQiBEIg8FgMARiBMJgMBgMgRiBMBgMBkMgRiAMhl0IEZmt05YbDP2NEQiDwWAwBGIEwjCkEZHJzqI6tzsLzSwRkU+LyNPO4iuHOz/POJlRn9EpLETkGyJyh/P5YOf4dMh1Ms5CL88555nrbH9WRA707LdcRGaEXdN3zqNF5E/OzwsiMqx/7pJhyKKUMj/mZ8j+AJOxUzMcjN1heh64AzsL5lzsFAbDgbiz/6eBB5zPFvBb4BRgJfCPEdf5d+w0DwAjgNewM45eAnzP2f4R4DXnc9g1ZwOPOp+X6msCWb2/+TE/9frRWQ4NhqHMWqXUSwAi8hfslbmUiLyELSBNwJ0iMhU7VXICQClVEJGzgReBW5RST0dc41jszLLfcr43YufNuRf4H+x8OqcB9zl/D7ymj6eB60VkCfCgUmp9LZU3GMIwLiaDAbzJ2wqe7wXstRW+D7QoeynLk7Abd81UoBV7DYIoBDhVKTXd+ZmolHpF2QvdbBGRQ7ATzd3t7B91TQCUUj/ATkiXAlaIyH4V19hgqAAjEAZDeZqAt53PZ+uNItKEvfbzUcBoEfmn0kNdHgcucrJrIiKHef52N/aqd03akgm7phcR2Vsp9ZJS6hpsF5cRCENdMQJhMJTnWuBqEXkaOy285gbgJqXUa8B5wA9EJGwZz+9ju4leFJHVznfN/djrVdxbwTW9LHQC438G2oFlVdbLYIjEpPs2GAwGQyDGgjAYDAZDIGYUk8FQR0TkHOBi3+anlVJf2xHlMRj6gnExGQwGgyEQ42IyGAwGQyBGIAwGg8EQiBEIg8FgMARiBMJgMBgMgfx/p6ZV8XzG4skAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models_ks('max_evals', space, ks_trains, ks_tests)\n",
    "models_auc('max_evals', space, auc_trains, auc_tests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b5078a32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 打印全部的迭代数据点太多，可选择打印出指定区间内的数据\n",
    "i = 20\n",
    "j = 30\n",
    "models_ks('max_evals', space[i:j], ks_trains[i:j], ks_tests[i:j])\n",
    "models_auc('max_evals', space[i:j], auc_trains[i:j], auc_tests[i:j])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4014d49b",
   "metadata": {},
   "source": [
    "## 7.选出最优的参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2fbbb213",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 可以选取迭代的任一步的参数\n",
    "# fmin出来的best是在第20次迭代，根据上面的图，综合查看auc和ks效果，选择83对应的参数进行下一步的手动精调\n",
    "\n",
    "param = trials_result[21]['misc']['vals']\n",
    "param_best = {k:v[0] for k,v in param.items()}\n",
    "\n",
    "fixed_params.update(param_best)\n",
    "fixed_params['max_depth'] = int(fixed_params['max_depth'])\n",
    "fixed_params['feature_num'] = int(fixed_params['feature_num'])\n",
    "fixed_params['min_child_samples'] = int(fixed_params['min_child_samples'])\n",
    "fixed_params['min_child_weight'] = int(fixed_params['min_child_weight'])\n",
    "fixed_params['num_leaves'] = int(fixed_params['num_leaves'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "470db3db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据选出的参数训练模型，得到最终的features\n",
    "# 在用贝叶斯得到的参数拟合模型后，实际有效的特征数由314降到85，这是由于过拟合的参数会限制特征数\n",
    "feature_num = int(fixed_params.get('feature_num'))\n",
    "chosen_final_feature = lgb_rfe_features[:feature_num]\n",
    "\n",
    "X = X[chosen_final_feature]\n",
    "X_test = X_test[chosen_final_feature]\n",
    "\n",
    "hit_indices = range(0,1000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf9dde8",
   "metadata": {},
   "source": [
    "## 8.手动调参"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c1e29478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.8,\n",
       "               feature_num=106, importance_type='gain',\n",
       "               learning_rate=0.010493853935646084, max_depth=4,\n",
       "               min_child_samples=1490, min_child_weight=0, min_split_gain=5.2,\n",
       "               n_estimators=25, n_jobs=-1, num_leaves=2, objective='binary',\n",
       "               random_state=7, reg_alpha=4.080643870666393,\n",
       "               reg_lambda=1.0474364956386228,\n",
       "               scale_pos_weight=2.425688073394496, silent=True, subsample=0.8,\n",
       "               subsample_for_bin=200000, subsample_freq=0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X.loc[X.index.isin(hit_indices)]\n",
    "Y = Y.loc[Y.index.isin(hit_indices)]\n",
    "X_test = X_test.loc[X_test.index.isin(hit_indices)]\n",
    "Y_test = Y_test.loc[Y_test.index.isin(hit_indices)]\n",
    "\n",
    "lgbm_tuner = LGBModelTuner(LGBMClassifier(**fixed_params), X, Y, X_test, Y_test, hit_indices)\n",
    "\n",
    "params = lgbm_tuner.params\n",
    "lgbm_tuner.get_model_result(params)\n",
    "lgbm_tuner.estimator #查看当前模型的参数组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8450efd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "{'boosting_type': 'gbdt',\n",
    " 'class_weight': None,\n",
    " 'colsample_bytree': 0.8,\n",
    " 'importance_type': 'gain',\n",
    " 'learning_rate': 0.05382503379196588,\n",
    " 'max_depth': 4,\n",
    " 'min_child_samples': 1880,\n",
    " 'min_child_weight': 230,\n",
    " 'min_split_gain': 1.2000000000000002,\n",
    " 'n_estimators': 473,\n",
    " 'n_jobs': -1,\n",
    " 'num_leaves': 5,\n",
    " 'objective': 'binary',\n",
    " 'random_state': 7,\n",
    " 'reg_alpha': 5.026868705176731,\n",
    " 'reg_lambda': 9.150313707092932,\n",
    " 'silent': True,\n",
    " 'subsample': 0.8,\n",
    " 'subsample_for_bin': 200000,\n",
    " 'subsample_freq': 0,\n",
    " 'scale_pos_weight': 1,\n",
    " 'feature_num': 57}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "dec73390",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'ks': (0.2886070131899444, 0.16805437150705688),\n",
       " 'auc': (0.6839661749161267, 0.5877166808752488)}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 调max_depth\n",
    "# max_depth是提高精确度的最重要的参数，一般会选取3-5\n",
    "lgbm_tuner.try_tune('max_depth', [1,2,3,4,5,6,7])\n",
    "lgbm_tuner.tune('max_depth', 4)\n",
    "lgbm_tuner.get_model_result(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2d39f3ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'ks': (0.2886070131899444, 0.16805437150705688),\n",
       " 'auc': (0.6839661749161267, 0.5877166808752488)}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 调num_leaves \n",
    "# 大致换算关系：num_leaves = 2^(max_depth)，但是它的值的设置应该小于 2^(max_depth)，否则可能会导致过拟合。\n",
    "lgbm_tuner.try_tune('num_leaves', [2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32])\n",
    "# lgbm_tuner.try_tune('num_leaves', np.arange(2,32,1))\n",
    "\n",
    "lgbm_tuner.tune('num_leaves', 2)\n",
    "lgbm_tuner.get_model_result(params)\n",
    "\n",
    "#小于2^4 =16 在 8之后 auc不再变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3a0c19aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调min_child_weight/min_sum_hessian_in_leaf \n",
    "# 调完精度的参数，开始调控制过拟合的参数\n",
    "# min_sum_hessian_in_leaf：也叫min_child_weight,\n",
    "# 使一个结点分裂的最小海森值之和（Minimum sum of hessians in one leaf to allow a split. Higher values potentially decrease overfitting）,可以很好的控制过拟合。\n",
    "# min_child_weight与min_sum_hessian_in_leaf只需要调节一个就好，两者是同一个东西。\n",
    "#样本量的10%左右\n",
    "# 10- 500 每隔10 看一看 在哪里ks降的比较厉害  range(10,500,10)\n",
    "lgbm_tuner.try_tune('min_child_weight', [10,20,30,40,60,80,100,120,140,160,180,200]) #先粗调后细调，参数范围不要思维定式\n",
    "# lgbm_tuner.try_tune('min_child_weight', np.arange(110,130,1))\n",
    "lgbm_tuner.try_tune('min_child_weight', np.arange(40,60,1))\n",
    "\n",
    "lgbm_tuner.tune('min_child_weight', 46)\n",
    "lgbm_tuner.get_model_result(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "58b5e4a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调min_data_in_leaf/min_child_samples 这两者是同一个参数，只需要调节一个就好\n",
    "# min_data_in_leaf 是一个很重要的参数, 也叫min_child_samples，指定了叶子节点向下分裂的最小样本数，它的值取决于训练数据的样本个树 和num_leaves.\n",
    "# 将其设置的较大可以避免生成一个过深的树, 但有可能导致欠拟合。可设置取值范围在 样本数/num_leaves附近调节。\n",
    "# 样本量的10%左右， 大数据集，一般会设置在千级以上\n",
    "lgbm_tuner.try_tune('min_child_samples', [100, 500, 1000,1500, 2000,2500, 3000,3500])\n",
    "lgbm_tuner.try_tune('min_child_samples', [1300,1500,1700,1900,2000,2200,2400,2600,2800,3000])\n",
    "lgbm_tuner.try_tune('min_child_samples', np.arange(500,1500,10))\n",
    "lgbm_tuner.try_tune('min_child_samples', np.arange(1800,2200,5))\n",
    "\n",
    "lgbm_tuner.tune('min_child_samples', 2500)\n",
    "lgbm_tuner.get_model_result(params)\n",
    "\n",
    "lgbm_tuner.estimator #查看当前模型的参数组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "def63f3a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69246ab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 调正则化参数lambda_l1:reg_alpha \n",
    "# 毫无疑问，是降低过拟合的，\n",
    "lgbm_tuner.try_tune('reg_alpha', [1,2,3,4,5,6,7,8,9,10])\n",
    "lgbm_tuner.try_tune('reg_alpha', np.arange(1,10,0.05))\n",
    "lgbm_tuner.try_tune('reg_alpha', [8.1,8.2,8.3,8.4,8.5,8.6,8.7,8.8,8.9,9])\n",
    "lgbm_tuner.tune('reg_alpha', 5)\n",
    "lgbm_tuner.get_model_result(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96945f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 正则化参数lambda_l1:reg_lambda \n",
    "lgbm_tuner.try_tune('reg_lambda', [1,2,3,4,5,6,7,8,9,10])\n",
    "lgbm_tuner.try_tune('reg_lambda', np.arange(1,10,0.05))\n",
    "lgbm_tuner.try_tune('reg_lambda', [1.8,1.9,2,2.1,2.2,2.3,2.4,2.5,2.6,2.7])\n",
    "lgbm_tuner.tune('reg_lambda',3)\n",
    "lgbm_tuner.get_model_result(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98d27ed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 调min_split_gain\n",
    "# 执行切分的最小增益，也是控制过拟合的参数，值越大，对过拟合控制越明显，设置过大会欠拟合 1 - 4 ？\n",
    "# 这个参数也看出数据的质量，计算的增益不高，就无法向下分裂，如果设置的深度很深但无法向下分裂， LGBM会给出warning，无法找到可分裂的，说明数据质量达到了极限\n",
    "lgbm_tuner.try_tune('min_split_gain', [1,2,4,6,8,10,12,14,15])\n",
    "lgbm_tuner.try_tune('min_split_gain', np.arange(1,4,0.1))\n",
    "lgbm_tuner.try_tune('min_split_gain', [3.9,4,4.1,4.2,4.3,4.4,4.5,4.6,4.7,4.8,4.9])\n",
    "lgbm_tuner.tune('min_split_gain',2.2)\n",
    "lgbm_tuner.get_model_result(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6a29380",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 调learning_rate \n",
    "# 在调完所有的参数后，再来对learning_rate进行调节， 0.01 - 0.2 \n",
    "lgbm_tuner.try_tune('learning_rate', [0.02, 0.04, 0.06, 0.08, 0.1,0.12,0.14,0.16])\n",
    "lgbm_tuner.try_tune('learning_rate', np.arange(0.17,0.19,0.005))\n",
    "lgbm_tuner.try_tune('learning_rate', np.arange(0.001,0.015,0.001))\n",
    "lgbm_tuner.tune('learning_rate', 0.008)\n",
    "lgbm_tuner.get_model_result(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33971ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 350 以内\n",
    "lgbm_tuner.try_tune('n_estimators', [40,60,80,100,120,140,160, 180, 200, 220,240,260,280,300])\n",
    "lgbm_tuner.try_tune('n_estimators', [10,20,30,40,50,60,70,80])\n",
    "lgbm_tuner.try_tune('n_estimators', np.arange(80,100,1))\n",
    "lgbm_tuner.get_model_result(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9eaedfce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 联合调learning_rate 与 n_estimators， n_estimators控制决策树的数量，而learning_rate是梯度下降的步长参数。\n",
    "# learning_rate可以用来控制梯度提升学习的速度，一般值可设在 0.01 和 0.3 之间。一般做法是先用稍多一些的子树比如1000，并设一个较低的learning_rate\n",
    "# 然后通过early_stopping找到最优迭代次数\n",
    "df_train_auc, df_test_auc = tune_n_estimators_learning_rate(lgbm_tuner,\n",
    "                                                    learning_rate=[0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1],\n",
    "                                                    n_estimators=[80,90,100,110,120,140,160])\n",
    "plot_n_estimators_learning_rate(df_train_auc, df_test_auc)\n",
    "\n",
    "lgbm_tuner.tune('n_estimators', 260)\n",
    "lgbm_tuner.tune('learning_rate',0.04)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f0c0794",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取当前参数\n",
    "# 最终模型\n",
    "# 查看调参结束后模型效果以及参数情况\n",
    "params = lgbm_tuner.params\n",
    "lgbm_tuner.get_model_result(params)\n",
    "lgbm_tuner.estimator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "479590e7",
   "metadata": {},
   "source": [
    "## 9.重新对rfe对特征排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e54b89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 调好的参数对特征再次排序\n",
    "lgbm_model = LGBMClassifier(**params)\n",
    "rfe = RFE(lgbm_model, n_features_to_select=1, step=1, verbose=1) #终止时特征数1个\n",
    "rfe.fit(X, Y)\n",
    "rfe_rank_2 = rfe.ranking_\n",
    "rfe_lgb_df_2=pd.DataFrame(rfe_rank_2,index=chosen_final_feature)\n",
    "\n",
    "rfe_lgb_df_2=pd.DataFrame(rfe_rank_2,index=chosen_final_feature_df.iloc[:,0])\n",
    "\n",
    "rfe_lgb_df_2 = rfe_lgb_df_2.rename(columns={0:'rank_lgb_rfe'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9820505",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看特征数对模型效果的影响\n",
    "space,ks_trains, ks_tests, auc_trains, auc_tests = tune_feature_num(params, X, Y, X_test, Y_test, rfe_lgb_df_2,\n",
    "                                                                    hit_indices,min_feature_num=5, step=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d3ab42f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#手动输入想看的区间\n",
    "i, j = space.index(50), space.index(60)\n",
    "models_ks('feature_num', space[i:j], ks_trains[i:j], ks_tests[i:j])\n",
    "models_auc('feature_num', space[i:j], auc_trains[i:j], auc_tests[i:j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77b23410",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 选择最终的特征数  手动修改下面的数字哦\n",
    "chosen_final_feature_rfe = rfe_lgb_df_2.loc[rfe_lgb_df_2.rank_lgb_rfe<=67].index.tolist()\n",
    "lgbm_tuner = LGBModelTuner(LGBMClassifier(**params), X[chosen_final_feature_rfe], Y, X_test[chosen_final_feature_rfe], Y_test, hit_indices)\n",
    "lgbm_tuner.get_model_result(params)\n",
    "lgbm_tuner.estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7505467",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 可根据最终的特征数再一次的调优\n",
    "\n",
    "#hit_indices = get_hit_indices(Data, flags)\n",
    "# hit_indices=  [i for i in range(len(Data))]\n",
    "hit_indices = range(0,1000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "783d039a",
   "metadata": {},
   "source": [
    "## 10.使用调好的参数进行模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce129f80",
   "metadata": {},
   "outputs": [],
   "source": [
    "import perform as pf\n",
    "from sklearn.metrics import roc_curve,auc\n",
    "from lightgbm.sklearn import LGBMRegressor\n",
    "\n",
    "lgbm_model = LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=0.8, feature_num=111, importance_type='gain',\n",
    "               learning_rate=0.11155463487315995, max_depth=5,\n",
    "               min_child_samples=2500, min_child_weight=61, min_split_gain=1.0,\n",
    "               n_estimators=181, num_leaves=14, objective='binary',\n",
    "               random_state=7, reg_alpha=1.5275404233282286,\n",
    "               reg_lambda=2.728606507333034, scale_pos_weight=1, subsample=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a17b289a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练\n",
    "train = X.copy()\n",
    "train_y = Y.copy()\n",
    "\n",
    "X_use=train[chosen_final_feature_rfe]\n",
    "X_test_use=X_test[chosen_final_feature_rfe]\n",
    "lgbm_model.fit(X_use,train_y,eval_metric='auc')\n",
    "preds_train = lgbm_model.predict(X_use) \n",
    "ks_value,bad_percent,good_percent=pf.cal_ks(-preds_train,train_y,section_num=20)  \n",
    "max_ks0=np.max(ks_value)\n",
    "false_positive_rate, recall, thresholds = roc_curve(train_y, preds_train)\n",
    "roc_auc0=auc(false_positive_rate,recall) \n",
    "\n",
    "print('当前模型在样本内训练集的KS值和AUC值分别为{0}'.format([max_ks0,roc_auc0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35f38ff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.hist(preds_train)\n",
    "plt.ylabel('Number of samples')\n",
    "plt.xlabel('probability of y=1')\n",
    "plt.title('Probability Distribution on train samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9450cbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(list(range(0,21)),np.append([0],bad_percent),'-r',label='Bad Percent')\n",
    "plt.plot(list(range(0,21)),np.append([0],good_percent),'-g',label='Good Percent')\n",
    "plt.plot(list(range(0,21)),np.append([0],ks_value),'-b',label='KS value')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('% of total Good/Bad')\n",
    "plt.xlabel('% of population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e277ddf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(false_positive_rate, recall, 'b', label='AUC = %0.2f' % roc_auc0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([0.0,1.0])\n",
    "plt.ylim([0.0,1.0])\n",
    "plt.ylabel('Recall')\n",
    "plt.xlabel('Fall-out')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d25c1106",
   "metadata": {},
   "source": [
    "## 11.模型测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4880ad80",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid = X_test.copy()\n",
    "valid_y = Y_test.copy()\n",
    "\n",
    "X_test_use=valid[chosen_final_feature_rfe]\n",
    "lgbm_model.fit(X_use,train_y,eval_metric='auc')\n",
    "preds_test = lgbm_model.predict(X_test_use)\n",
    "ks_value,bad_percent,good_percent=pf.cal_ks(-preds_test,valid_y,section_num=20)\n",
    "max_ks0=np.max(ks_value)\n",
    "\n",
    "false_positive_rate, recall, thresholds = roc_curve(valid_y, preds_test)\n",
    "roc_auc0=auc(false_positive_rate,recall) \n",
    "\n",
    "print('当前模型在样本内测试集的KS值和AUC值分别为{0}'.format([max_ks0,roc_auc0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "570596c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.hist(preds_test)\n",
    "plt.ylabel('Number of samples')\n",
    "plt.xlabel('probability of y=1')\n",
    "plt.title('Probability Distribution on test samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a630c7d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(list(range(0,21)),np.append([0],bad_percent),'-r',label='Bad Percent')\n",
    "plt.plot(list(range(0,21)),np.append([0],good_percent),'-g',label='Good Percent')\n",
    "plt.plot(list(range(0,21)),np.append([0],ks_value),'-b',label='KS value')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('% of total Good/Bad')\n",
    "plt.xlabel('% of population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03281037",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(false_positive_rate, recall, 'b', label='AUC = %0.2f' % roc_auc0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([0.0,1.0])\n",
    "plt.ylim([0.0,1.0])\n",
    "plt.ylabel('Recall')\n",
    "plt.xlabel('Fall-out')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75c173f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "basic_badrate = sum(Y) / len(Y)\n",
    "point0 = 600\n",
    "odds0 = basic_badrate / (1 - basic_badrate)\n",
    "PDO = 25\n",
    "B = PDO / np.log(2)\n",
    "A = point0 + B * np.log(odds0)\n",
    "point_train = A - B * np.log(preds_train / (1 - preds_train))\n",
    "point_test = A - B * np.log(preds_test / (1 - preds_test))\n",
    "\n",
    "#point_train[np.where(np.array(hit_indices) != 1)[0]] = np.NaN\n",
    "#point_test[np.where(np.array(flag_test_all) != 1)[0]] = np.NaN\n",
    "point_train[np.where(point_train > 1000)[0]] = 1000\n",
    "point_train[np.where(point_train < 300)[0]] = 300\n",
    "point_test[np.where(point_test > 1000)[0]] = 1000\n",
    "point_test[np.where(point_test < 300)[0]] = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb49ebce",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.hist(point_train)\n",
    "plt.ylabel('Number of samples')\n",
    "plt.xlabel('Score')\n",
    "plt.title('Distribution on train samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00465319",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.hist(point_test)\n",
    "plt.ylabel('Number of samples')\n",
    "plt.xlabel('Score')\n",
    "plt.title('Distribution on test samples')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc171132",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算PSI\n",
    "PSI_value = pf.PSI(preds_train, preds_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3582638d",
   "metadata": {},
   "source": [
    "## 12.生成部署代码  保存if_else部署代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c2e8fa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_file_path=r'E:\\模型开发'\n",
    "import os\n",
    "os.chdir(output_file_path)\n",
    "\n",
    "from model_parser import LGBModelParser\n",
    "\n",
    "p = LGBModelParser(lgbm_model, chosen_final_feature_rfe)\n",
    "# p = LGBModelParser(lgbm_model, chosen_feature)\n",
    "\n",
    "# p = LGBModelParser.load_from('lgbm.model', 'lgbm.feature')\n",
    "p.parse(\"if_else1.py\", lang=\"python\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b77087a9",
   "metadata": {},
   "source": [
    "## 13.保存最终模型结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bcd2238",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''output_file_path是输出lgbm模型文件和所选变量的文件夹路径，按照实际情况调整'''\n",
    "output_file_path=r'E:\\模型开发'\n",
    "import os\n",
    "os.chdir(output_file_path)\n",
    "\n",
    "lgbm_model.booster_.save_model('raw_lgbm1.model') \n",
    "chosen_feature_pd=pd.DataFrame(chosen_final_feature_rfe)\n",
    "chosen_feature_pd.to_csv('lgbm_features1.csv',encoding=\"utf_8_sig\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f19531f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "631c675c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f43ad4ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3efcd7a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76a381a2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fb456ae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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